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The dilemma of advice
Leigh Caldwell
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Some colleagues at Irrational Agency and I have recently been working on a framework around advice, and a key dilemma:
  • Those who know less about finance, are more in need of advice
  • But those who know less about finance, are less likely to realise they need advice
In some ways this is another form of Dunning-Kruger syndrome. But there's an interesting additional twist. Because advice is an information good, you can't make an informed decision about it before receiving it, because you don't know what the advice will be. And once you've received it, you may think you don't need it, because now you possess all the knowledge that the advice gave you!
So an element of trust is required to make the decision to buy advice, as well as an element of self-knowledge. Neither of these things are in abundant supply in the finance industry.
An outline of our analysis follows:
  1. The context: more and better quality investment is needed. Consumers need to make better financial returns in order to finance retirement in an ageing society; and the economy (especially the UK economy) needs higher investment to increase growth beyond its current anaemic level.
  2. The problem: The bounded rationality of retail investors presents a barrier to achieving higher investment outcomes.
    1. Investors have cognitive biases that can reduce investment beneath its optimal level:
      1. Risk aversion: many people don't like the risk of equity investment even if its average returns are higher
      2. Loss aversion: many people are unwilling to accept any losses, and would rather sacrifice future growth in order to avoid the possibility of their capital being affected
      3. Ambiguity aversion: nobody can say for sure what will happen to an investment, and this ambiguity is too much for some people.
    2. Several cognitive limits also reduce people's likelihood to invest
      1. Lack of comprehension: people don't understand the nature of investments, especially around concepts like diversification
      2. Limited calculation capability: people aren't good at understanding the long-term compounding effect of higher or lower returns and fees
      3. Lack of planning ability: people don't plan their future actions well
      4. Lack of capability to commit to intrapersonal agreements with future self
  3. Several possible solutions can help solve these challenges:
    1. Personal cognitive tools: if investors can adopt new mindsets or can train themselves to overcome their cognitive barriers, they can make better choices. These tools might include:
      1. Rules to understand compounding (e.g. the rule of 72)
      2. Training tools such as Serious Games to help people become more resilient to risk and loss
    2. Personally selected external tools: Investors might voluntarily adopt tools to help them overcome their own biases. For example:
      1. automatic deposit into an investment account
      2. term commitments that don't allow for withdrawals in case the investor becomes fearful
      3. diversified low-fee funds that automatically diversify
    3. Finally, institutions might offer externally imposed structures that move people towards optimal decisions, outside of their own free choice:
      1. Tax and similar incentives: many countries offer short-term tax relief or a reduced overall tax burden in return for increased savings
      2. Forced saving: taxation in return for guaranteed future income, which may or may not be directly tied to the amount saved
      3. Cultural pressures e.g. "the housing ladder": cultural norms that push people towards saving more than they might otherwise be inclined to do
    4. As a special case of the institutional/external offering, various forms of advice can change people's behaviour and encourage higher levels of investment or more effective investment choices.
      1. Informal advice (non-professional, peer-to-peer): social information sharing or other pressures that encourage people to invest
      2. Informal advice (journalistic, internet): information from media that is often free or low-cost, and generally comes without liability to the offerer, except for any risk to their reputation
      3. Formal advice, in which an adviser personally talks to the investor and makes recommendations, which are typically different to what the investor would do on their own. These fall into three categories:
        1. Independent and paid for by the investor
        2. Independent and paid for by commission on products
        3. Non-independent: offered alongside product
      4. Guidance (non-tailored), in which guidance might be prepared for a class of investors or those with particular characteristics, but is not produced in consultation with the individual who is using it.
      5. Guidance/advice (algorithmic/AI), in which an advice algorithm may be created which can produce customised recommendations based on an individual's data or answers to a questionnaire, but no individual adviser is involved.
  4. A model of agency and influenced decisions
    1. To explore the optimal combination of individual agency, intervention and advice, we can build a model that incorporates the likely outcomes of investment with and without intervention, as well as a factor addressing the process utility (positive or negative) that some individuals may experience from the interventions themselves.
    2. Let us assume a set of investment instruments I = {i}, each of which has an average annual return r[i] and an expected variance v[i].
    3. Each investor A has a 'natural' bundle of instruments B they would buy without intervention.
    4. Each form of intervention can be defined as a function F:A,B->B. Executing F on investor A who would naturally choose B, results in A owning a different bundle of investments F(A,B).
    5. We can calculate the total return and total variance of B and F(A,B), subtracting the former from the latter as an aggregate estimate of the impact of the intervention, rF and vF. The impact may be different depending on the characteristics of A.
    6. Each intervention F also has a price, which we assume to be deducted from the value of the investment portfolio prior to purchase of a bundle.
    7. Finally, F also has a process utility u(F,A) which is experienced by the investor. Sometimes, this may be a positive number (for example, if an investor feels supported by the adviser and after the process is happier and less stressed about the disposition of their investments). In other cases, it might be negative (for example, if a portion of income is deducted and invested on their behalf, and they object to the reduction in personal choice this represents).
    8. The challenge for investors is that the impact of F, and in some cases the process utility u, are not observable by them in advance. To the extent that advice outcomes are correlated between investors, or that the intervention process is highly transparent, it may be possible for them to observe other people's outcomes and estimate the impact for themselves.
    9. Given this, there is a probability 0 < p(F,A) < 1 that each intervention will be selected by an investor (even for mandatory interventions, investors may in theory arrange their affairs so that they are not subject to the intervention, for instance by reducing their income or moving offshore). The weighted impact of each intervention on total return can be defined as the sum of p rF across all investors. To evaluate the effectiveness of an intervention F at the systemic level, we would compare the impact of all interventions in a system with and without F; a single F might itself have a positive impact but may change the likelihood of investors adopting other interventions, so can't necessarily be studied in isolation.
  5. Evidence
    1. We have an expanding reference list informing the various items incorporated in the above model; please contact me if you're interested in seeing this or discussing further.
    2. Detailed evidence on the estimation of the model and the impact of specific interventions will require primary data collection.
  6. Conclusions
    1. To determine the impact of adding a new intervention (for example a new type of advice, or a change in taxation) a model of consumer behaviour in the presence and absence of the intervention is required.
    2. This model can be constructed from a combination of data on existing investor behaviour and newly collected data from investment simulation surveys.
    3. A choice is also required: how much weight to place on process utility versus total return. Investors may value their autonomy and feel that the imposition of an intervention, even with a positive expectation value, is harmful to them. Alternatively, investors may have different views to government on the future returns of an instrument and therefore the impact of an intervention (for instance, a ban on cryptocurrency "to protect investors" might have a positive expectation value in the government's modelling, but keen crypto speculators may predict a negative expectation value).
    4. Expectation values themselves can form part of an instrument, so there may be room to develop "intervention arbitrage" tools that can allow interventions to become a Pareto improvement.


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Dead rats and dopamine - a new publication
Leigh Caldwell
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My new paper, coauthored with Yohan John, Dakota McCoy and Oliver Braganza, is out in Behavioral and Brain Sciences.

"Dead rats, dopamine, performance metrics and peacock tails" is about the universal emergence of proxy failure. When you measure and incentivise performance by a single metric (a proxy), the proxy will always become a worse measure of performance than it was before you added the incentive.

This effect is also known as Goodhart's Law in economics, and by other names in other fields - but the same underlying process drives the effect across multiple domains. Our paper studies it in management, economics, biology, neuroscience and other areas.

Recent concerns about AI alignment are closely related to this phenomenon. The paperclip problem is a good example - if a sufficiently clever AI is given a single goal, to produce as many paperclips as possible, it may eventually destroy all of humanity and take over the whole universe in its efforts to maximise output.

Fortunately, our paper identifies a few constraints that can stop systems from running completely out of control - we might still be safe from our Clippy overlords.

Currently the journal is inviting comments on the article - a number of authors will be invited to write a commentary which will be published in the journal alongside the article. Please do feel free to sign up on the journal website and submit your proposal for a comment if you have something to add to the conversation.

It has been a long and very satisfying process for the four of us to put this paper together, so I hope you enjoy it - I would love to hear your informal thoughts if you don't want to go so far as submitting a formal response to the journal.

If you can't download the paper via the link above please let me know and I can send you a preprint copy.

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How to debunk an electoral fraud claim
Leigh Caldwell
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There are plenty of claims of US election fraud floating around this week. Most of them fall into three categories:

  1. Too vague to be meaningfully evaluated or investigated
  2. Too small to matter (a few individual ballots being challenged here and there, possibly valid but not enough to affect the results)
  3. Too wild to stand up to any kind of scrutiny
Together, these claims are certainly problematic: they create a fog of doubt about the legitimacy of the democratic process.
But a fourth category is more insidious. A twitter mutual retweeted the thread quoted below. You can click through to read the whole thread, but I have embedded the highlights. It's a well-told story, with several characteristics that make it effective - as well as dangerous.

To start with, the data comes from an authoritative source, the New York Times. Even better in this case: a source associated with "the other side". Surely the liberals can't deny the truths from their own newspaper?

The following information is provided via an anonymous data scientist and another anonymous individual who wrote a script to scrape the national ballot counting time series data of off the @nytimes website.

— CulturalHusbandry (@APhilosophae) November 9, 2020

But, the writer implies, the Times wouldn't want you to know this - it's only because we have cleverly figured out how to get access to their secret data (very enticing):

This is based on their proprietary "Edison" data source which would ordinarily be impossible to access for people outside the press.

— CulturalHusbandry (@APhilosophae) November 9, 2020

The source code used to generate the charts is provided, allowing any reader to 'check the workings' (of course, almost nobody will). A conspiratorial suggestion to download the programs because - presumably - the government will inevitably delete them if they reveal the truth.

I suggest that everyone back up both of these files, bc this is an extremely important data source, and we cant risk anyone taking it down.

— CulturalHusbandry (@APhilosophae) November 9, 2020
(in fact, the provided source code is incomplete - it pulls the data down from the New York Times but it does not do any of the analysis used to produce the graphs later in the thread. Perhaps they don't want us to check the workings after all?)
The thread starts off in a neutral tone, holding back on its strongest claims until later. This allows the evidence to gradually persuade; your mind's skepticism is not activated by any bold accusations.

Charts and data lend authority to any claim, as they appear to provide evidence that validates the words used to describe them:

One of the first things noticed while exploring the dataset is that there seems to be an obvious pattern in the ratio of new #Biden ballots to new #Trump ballots. pic.twitter.com/jJAAF6Pp9X

— CulturalHusbandry (@APhilosophae) November 9, 2020


A series of near-reveals are followed by a retreat from the precipice - they tease us with a hint that we have found the fraud...only to pull back.

How could this be possible? Is this a telltale sign of fraud? Surprisingly, as it will be shown, the answer is no! This is actually expected behavior. Also, we can use this weird pattern in the ballot counting to spot fraud!

— CulturalHusbandry (@APhilosophae) November 9, 2020

Good storytelling.

The explanations are well written, allowing readers to feel they are gaining a true understanding of what's going on. Your mind's desire to know, and flatter yourself with your knowledge, creates a confirmation bias where you increasingly go along with what is being claimed.


like a deck of cards. Since the ballots are randomly mixed together during transport, spanning areas occupied by multiple voting demographics, we can expect the ratio of mail-in #Biden ballots to mail-in #Trump ballots will remain relatively constant over time...

— CulturalHusbandry (@APhilosophae) November 9, 2020

But finally, the dramatic buildup pays off.

Around 4am there, there is a marked shift in the ratio of D to R mail-in ballots. Based on other posts in this thread, this should not happen. This is an anomaly, and while anomalies are not always fraud, often they may point to fraud.

— CulturalHusbandry (@APhilosophae) November 9, 2020

so what happened just before 3am CST in Wisconsin? This did!https://t.co/t7DTmMwIOB

— CulturalHusbandry (@APhilosophae) November 9, 2020

Ta-da! A police car driving 169,000 absentee ballots to...somewhere. Fits nicely with narratives we might remember from old TV shows and historical rumour, about boxes of ballots being delivered by the Democratic "machine" just in time to swing an election.
Now we're on the downhill slope, and the storytelling can be ramped up for a couple of tweets before we get back to the data.

Around 3am Wisconsin time, a fresh batch of 169k new absentee ballots arrived. They were supposed to stop accepting new ballots, but eh, whatever I guess.

— CulturalHusbandry (@APhilosophae) November 9, 2020

(a clarification: they are only supposed to 'stop accepting' ballots that arrive in the mail after polls close. They're still meant to transport and count the votes that have already arrived - which these had.)
You've been waiting, so here's the "explanation" of the big reveal.

than the rest of the ballots quite possibly bc additional ballots were added to the batch, either through backdating or ballot manufacturing or software tampering. This of this being kind of analogous to carbon-14 dating, but for ballot batch authenticity.

— CulturalHusbandry (@APhilosophae) November 9, 2020
Sounds plausible, right? We'll get back to that.
To continue the story: if you aren't convinced by Wisconsin, let's look at Pennsylvania.

lets look at another anomaly: pic.twitter.com/L9cCqJ9mKB

— CulturalHusbandry (@APhilosophae) November 9, 2020
And Georgia. And Michigan. The repetition lulls you into belief. Sure, someone could probably explain the 'anomaly' in one state. But all five of them?
Finally Virginia is thrown into the mix. Same pattern. You haven't heard anything about Virginia this election, have you? Well that just goes to show:

Now in fairness, VA is the only state out of the 50 that has anomalies but has not had accusations of voter fraud, yet. I think this is the exception that proves the rule. Yet to figure out what causes this anomalous shift, but here it is so no one accuses me of holding it back.

— CulturalHusbandry (@APhilosophae) November 9, 2020

I'm not sure what 'the exception that proves the rule' means, but it serves its rhetorical purpose. If they were trying to trick you, they wouldn't have included Virginia. The fact that they have, shows their transparency and rigor.
A final summary to reinforce the key message:

ballot return to be extremely UNIFORM in terms of D vs R ratio, but to drift slightly towards R over time bc some of those ballots travel farther. This pattern proves fraud and is a verifiable timestamp of when each fraudulent action occurred.

— CulturalHusbandry (@APhilosophae) November 9, 2020

Right! Quite a journey there. I can see why people are persuaded. But is any of it true?

The first thing that made me (and should make you) suspicious is that in each of these charts, the Democrat/Republican ratio of most batches of ballots is EXACTLY the same (apart from the so-called anomalies). Not just nearly, but exactly the same. The purported explanation is that they were 'shuffled in the mail like a deck of cards'. Well, anyone who has ever shuffled a deck of cards knows that this isn't what it looks like.
If this was true, a well-shuffled pack would always deal everyone at the poker table an equal hand. There would be no chance of winning - or losing - at blackjack. Tossing a coin ten times would always produce exactly five heads and five tails. But mathematical randomness does not work like that.
Anyone who works with real-world data will recognise that flat graphs like that are never what we see in reality.
In reality, a shuffled pack produces a distribution of outcomes clustered around the average. Sometimes it's higher, sometimes lower, and occasionally it's the exact average. It's not smooth, and there's no way every single batch would have an identical ratio. The probabilities are governed by the binomial distribution, and a real chart of these returns would look less like this (from the Pennsylvania graph above):



and more like this:





This chart was generated from a perfectly shuffled "deck" with exactly 50/50 proportions of each party's votes. It's fairly smooth - but not flat like the charts in the thread. The ratio of votes is never identical from one batch to the next, because real randomness is noisy.

The second reason to be suspicious of the original charts is that they do not reflect how votes are actually counted in those states. Ballots are not mailed into the state capital and shuffled together. Instead, they are mailed into a central point for each county. And every county has different politics, and a very different ratio of Democrat to Republican voters.
If you were watching the results on Tuesday night, Wednesday or Thursday you will remember the votes coming in from different counties - Waukesha, Kenosha, Milwaukee, Philadelphia, Allegheny, Maricopa - each with its own profile. Some are more Democrat and some are more Republican. Some got more of their reports in earlier in the night, others later. Mostly they got in-person votes counted first and mail ballots later. By 3am CST, more of the Republican-leaning counties were in and more Democrat-leaning ones still out, but there were a number of batches from Republican counties still to come.
Therefore the real pattern might have looked more like this, with GOP counties mostly coming early and Dem batches dominating later on:




The smoothness of the charts in the original thread is an indication - not of fraud - but that the data is probably wrong. Under no plausible scenario, not even a fraudulent one, would those lines look so flat.
So I asked myself: is it a straightforward fake? Has the author of the thread simply made up the data entirely? That didn't feel quite right either. Time to have a closer look.
I downloaded the program posted in the thread (fortunately the CIA hadn't deleted it yet, phew) and took a look at where the data was coming from. Indeed, it is the New York Times:
  state_results = requests.get('https://static01.nyt.com/elections-assets/2020/data/api/2020-11-03/race-page/{}/president.json'.format(formatted_state)).json()
Replace {} with the name of a state in lowercase and you can download it yourself. Here's the Wisconsin version.
So the data is real. Could it simply have been misinterpreted? One possibility was that they might have charted the cumulative D/R share instead of the D/R share of individual batches of ballots. That would explain why they converge on a stable value, because the cumulative share in a swing state quickly reaches a level close to 50% and changes very slowly after that.
However, this doesn't fit either. A cumulative graph would not jump around in the early hours like the graphs in the original thread.
In any case, the psychology doesn't point that way. This was quite a painstaking piece of work, from someone with at least a passing understanding of statistics and the ability to read data. It seems unlikely they would have confused cumulative with batch data. And - feel free to debate this with me - I think this kind of task is most likely to be carried out by someone who believes in what they are doing. No doubt there are people, perhaps even elected officials in the US government, who will say whatever suits them regardless of whether they believe it. But someone who puts together a detailed analysis like this is probably sincere, at least on some level. I'm not saying they don't have an agenda, or a bias. However I do think they are trying to put together a real argument out of real data.
Therefore, I didn't believe the data was either faked or crudely misinterpreted. And yet, it couldn't possibly be saying what the author was claiming.
So I took the next step: recreating what the author had done, to see how far I could get.
My computer didn't have all the right python modules to run their program, so I downloaded them, updated the source code to run under Python 3 (interestingly, the original was written in Python 2, which is more than ten years old), and was able to run the program and recreate the data behind the graphs. So the data is real, the graphs are really produced by the program (plus a second program that was not included) - so what was going on?
To answer that, we have to look in more detail at what the graphs are actually showing us.
Here is a chunk of the relevant data from the Times:

(apologies for poor image resolution).
Each data entry tells us:
  • The share of votes counted for Biden (so far)
  • The share of votes counted for Trump (so far)
  • The total number of ballots counted so far
  • The most recent time that new ballots were added to the total
As you can see, the number of votes continually goes up - this is cumulative data. That is, each entry includes all the same votes counted in the previous entry, plus the new ones that have just arrived.
The author (or their anonymous data scientist/programmer friend) has converted these cumulative totals into a differential time series. They take each entry and subtract the previous one, to work out how many new votes were added in this batch, and figure out how many were for Trump and how many for Biden.
For instance, take the first two rows of data here:


With 213,227 votes counted, Biden has 50.8% and Trump 47.4%. That's 108,319 for Biden and 101,070 for Trump (and 4,000 or so for third parties or write-ins).
After the next batch, the total votes counted are 279,037: Trump has now pulled ahead with 49.2%, and Biden has 48.9%. That means - in total - Biden has 136,449 and Trump 137,286.
We can simply subtract each candidate's totals before and after, to find out how many votes they got in this batch. Biden got (136449 - 108319) = 28,130. Trump got (137286 - 101070) = 36,216. Total votes in the batch, including third parties, is 65,810. This gives Trump 55.0% and Biden 42.7% - most likely this came from one of the more rural pro-Trump counties.
Straightforward enough arithmetic, right? But can you spot the hidden fallacy in the reasoning? There is a landmine in these figures which makes it impossible to draw the conclusion that the anonymous tweeter wants to draw.
Let's look at another set of figures and see if we can spot the landmine there.

This is from a little later in the night, when the big initial vote batches have all been counted, and the updates are much smaller. Most of the new batches being reported are only one or two thousand ballots. Try the same exercise with the first two rows of this data.
After batch 1: Trump = 50.6% of 1,087,928 = 550,492. Biden = 47.7% = 518,942.After batch 2: Trump = 50.6% of 1,089,047 = 551,057. Biden = 47.7% = 519,475
New votes in batch: 1,119. Trump = 566, Biden = 534.
So what share of the votes in the new batch did each candidate get? Trump = 50.6% and Biden = 47.7%. Exactly the same as their vote share for the whole night up to this point.
This is surely an extreme coincidence! Whatever county these votes happened to come from, it has exactly the same breakdown of votes as the whole state? And it's not just that batch - the same pattern is seen with most of the vote batches in the above image.
If this were true, it would indeed indicate something weird was going on with the ballots. But it is not true. Have you spotted why yet?
It is all down to one simple reason: rounding errors.
The data provided by the New York Times is rounded to the nearest 0.1%. For its intended purpose, this is sufficient - they provide the data to support the graphics shown on their news pages, and news pages are fine with 50.6% vs 47.7% - it's close enough.
But the rounding means it is impossible to calculate the exact number of votes for Trump and Biden from the above data. The numbers are approximate, and could be off by 0.05% in either direction: in this case, 560 ballots either way. That doesn't affect the overall totals very much, but look at the number of votes in the individual batch we analysed above: 566 for Trump and 534 for Biden. If the totals in this batch are 560 off, there could be as few as 6 Trump votes in the batch, or more than 1000. That is, the real Trump share could be anywhere from 1% to 100%, and the Biden share 0 to 99%!
The rounding error makes it impossible to work out the vote share in each batch. And vote-share-per-batch is the one piece of information that the whole thread relies on, "anomalies" and all.
The original calculation, instead of treating the 50.6% figure on the first cumulative total as an approximation, treats it as an exact number. The next total also has a 50.6% score for Trump (which in itself is not surprising, since a small batch of new votes will not affect the cumulative share much). If you treat these as exact numbers instead of approximations, the calculation will tell you that the batch has exactly 50.6% Trump votes too, to keep the cumulative score identical.
In reality, that batch probably had somewhere between 400-700 Trump votes and 400-700 Biden votes - there is no way to know the real numbers. The proportions do not follow that artificially smooth line after all.
This calculating error also has some other strange consequences. Let's say the (real) Trump share is gradually creeping up, batch by batch, from 50.6% to 50.63% to 50.649% (all of which are rounded to 50.6%). A final tiny increase to 50.65% will cause the total to be rounded up to 50.7% instead of rounded down. It will appear that 1100 Trump votes have arrived all at once, when in reality they were spread over several batches. This makes it look as if there is a batch of votes almost completely dominated by Trump (or Biden - this effect goes either way). In fact, it could appear as if there are more Trump (or Biden) votes than the total number of ballots in the batch!
And this is exactly what you see in the original author's Wisconsin graph (look from midnight onwards): a long series of identical vote shares, and some wildly high and low outliers in both Trump's and Biden's direction. These outliers represent times when the vote share went up or down just enough for the total to be rounded up instead of rounded down. There are even bigger outliers than the ones shown, but the graph has been cut off at 2.00 presumably to make this less obvious.



So the mystery is solved. There is no "anomaly": just a genuine shift in vote share over the course of the night, because the votes that got counted later include a higher share of mail-in ballots (which take longer to count) and are from different counties. This shift is disguised by the rounding errors that these charts rely on.
Did the original author - or data scientist - realise this? Maybe not. Earlier I mentioned a Python module (it's called pandas - aw, cute) which they used for the data analysis. pandas has a time-series function which automatically works out the differences between batches - the calculation I did by hand above. Perhaps they called it without looking closely at the outputs, or thinking through the implications of the rounding. Maybe they didn't even realise the data was rounded (although it's fairly obvious). I hesitate to accuse someone of lying when it could be a mistake. But the upshot is the same: the whole set of claims made in this thread are wrong, and should be retracted.

Unfortunately there doesn't seem to be (yet) an authoritative source of the real cumulative vote counts which would allow us to create an accurate version of the above graph. Perhaps the Times or Edison will release that at some point. But in the absence of that, just think back to Tuesday night or Wednesday (or Thursday or Friday!) when you were watching the results come in. You may recall that every new batch of votes had a different share for Biden and Trump. In some reports (Arizona, for example) there were batches with a 51%, 57% or 59% Trump share. In Pennsylvania, most (not all) of the late-counted batches were pro-Biden, but by varying amounts - 60% in Allegheny, 73%, 75% or 79% in Philadelphia. The unpredictability of these vote shares was exactly what kept us waiting 4 days for the result to be called. Nobody knew what the Allegheny or Maricopa shares would be until they arrived, so we didn't know if the remaining votes would be enough to push either candidate over the edge and win the state.

If you'd like to see another view on this, here's a twitter thread from @cb_miller_, who independently did the same calculation as me. He has presented the same data in some alternative ways to show how the error works.

Some final thoughts, then - if you want to understand the truth of an assertion you see online, ask yourself:
  1. How good is the storytelling? If it follows a dramatic arc that confirms what you already suspect to be true, try to separate the persuasiveness of that rhetoric from the factual claims being made. Good storytelling doesn't mean that a claim is false, but it also doesn't mean it's true.
  2. Does the data look very clean and simple? Real data is usually a bit more messy, with the patterns not as easy to see.
  3. Does it accord with what you already know? In this case, a bit of thought about how counties report their vote totals would have shown that the claim could not be true.
  4. Does the claim rely on a long series of graphs and claims about data that are hard to check? If you don't have the expertise or time to check it yourself (and few of us do), see if someone you trust has checked and endorsed it for you. That might be a scientist, a trusted media organisation (although I'm aware that not everyone trusts traditional media organisations to the same degree) or a government agency (same). But even if you don't believe everything those authorities tell you, the fact that they are willing to put their name to something might still indicate they are willing to stake some of their credibility on it.
  5. A corollary of this: do you know the name of the person making the claim? Anonymous tweeters are not putting anything at stake by posting stuff like this, so you might choose to put less weight on what they say. Maybe different in a whistleblower situation where someone's safety is at risk, but that's why journalists protect their sources: so you can benefit from the fact-checking the journalist has done, without needing to know exactly where the original facts came from.
  6. Have you heard about it from anyone else? If something really is a genuine scandal, you'll probably hear about it in more than one place. It's much easier for someone to check the details of a report like this and amplify it, than for a whole conspiracy of journalists to get together and suppress it. And in a highly competitive media world, there are enough news sites with plenty of incentive to report any genuine issues - even if you think MSNBC wants to hide the truth, lots of other people don't.

Stay skeptical for sure, but eventually you have to trust someone. A good heuristic is: the more people involved in an activity, the harder it is to hide anything nefarious. There are a lot of people involved in counting votes in a US election, and it seems unlikely to me that widespread malarkey could go on without lots of people spotting it.


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Predictions for the next decade in Behavioral Science
TaraECooper
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Behavioral Scientist have put together their favourite predictions, ideas, worries and challenges in the field of behavioral science for the next decade. We were delighted they accepted our pitch on cogitive economics as one of the new oppportunities in the future!

Here is our prediction for the future - what is yours?

"The twenty-first century is pushing us toward an ever more digital, information-driven, persuasion-based global economy—just as a new set of tools are emerging in neuroscience and psychology that offer the power to understand these phenomena in a new way. Cognitive economics is a new field rooted in behavioral economics, paralleling the shift from behavioral to cognitive psychology. Rather than focusing on biases in choices between material goods, cognitive economists explore how people consume intangible products with their minds.

Consumers no longer strive to acquire only material goods or earn the most money. Instead, they seek purpose, symbolic value, intense experiences and convenience: all things consumed inside their heads.

The old economics cannot explain the huge value consumers place on these symbolic goods, mental states, thoughts, and beliefs. Although behavioural economics explores some of the psychology behind economic decisions, it still mainly deals in money and physical goods.

Cognitive economics considers our emotions, hopes, beliefs, fears, and ideas, asking how much does your internal mental experience matter to you? How much are they worth? And the biggest economic question of all: How can society deliver all of us the best possible outcomes?"

Don't miss out on familiar names adding in their thoughts on the next decade - George Loewenstein, Koen Smets and more. Full article here.
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Am I the person Dominic Cummings is looking for? A followup to The Times
Leigh Caldwell
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For those interested in a bit more background to my Times article today, here are some details on how Cummings's topics have showed up in my cognitive economics research. You can judge for yourself if I have spotted what he is working towards.

Starting with Judea Pearl's modelling of causality. Pearl developed a way of using graphs (a kind of diagram showing a network of relationships between objects – like the chocolate example below) to express and work out cause-and-effect relationships. For example, you might use them to determine whether smoking causes cancer, or carbon dioxide causes global warming – or more locally, whether cutting Universal Credit reduces unemployment.

Quite often, we find that when scientists discover something about the structure of the world, the human brain has got there before us. The brain has evolved to seek out cause-and-effect relations in the world around us, and assemble them into a graph just like this. It learns the relationships by observation: if you see the sun come up and feel warm, you will naturally assume that one causes the other.

Our brains contain millions of these cause-and-effect links and much of our mental activity is based on navigating around these graphs. (One reason we have to keep traversing the graphs is that it's hard to distinguish causality from correlation. We first learn correlations, and then use mental simulation – daydreaming, planning, storytelling – to try out different scenarios and learn which relationships are genuinely causal. Sometimes the brain gets this wrong, and we end up with superstitions instead of accurate causal knowledge.)



A side-effect of this process is that the brain teaches itself shortcuts. For example, early in life I learned that having money causes me to be able to buy chocolate, which causes happiness. Readers familiar with Pavlov and his dogs will spot the logical conclusion: I learned that money itself was rewarding, and eventually was able to gain pleasure just from having money, even without going out to buy the chocolate. In computer science terms, the graph "caches" reward at key points to save the effort of recalculating it every time it is needed.

By identifying the cached reward points in the causal graph we can understand a lot of modern economic behaviour. People place value on mental states that are not directly related to their material interests: honesty, pride, compassion, dignity, status, identity, uniqueness and so on. These objects, because they generate mental reward, can become just as valuable as money, food, warmth and the more basic objects of economic life. These objects can be seen as our true values: if we get more reward from compassion than pride, or vice versa, that tells us something about who we are.

The reward that is generated from these mental objects is what drives our mind's endless habit of wandering over the causal graph, checking and recalculating the links. This wandering is what we think of as our imagination: daydreaming, speculating about the future, immersing in fictional worlds or replaying the past. I call it the mind's "System 3" – the counterpart to the System 1 and 2 made famous by Kahneman.

This process can influence how we build AI. To make artificially intelligent algorithms think like humans, we need to give them motivations like humans. One way to do this is to give them a similar structure to human minds. The causal graph with its powerful ability to support reasoning and decision making is a good basis for AI. "Big data" approaches to AI start with a blank sheet – an empty graph – and try to fill in the blanks just by throwing more and more data at the computer.

A better approach, in my view, is to give the computers an initial graph based on the human mind. There are ways of measuring what this graph looks like for a person or group of people – an online test that takes a few minutes – and the computer can be given a copy of this graph as its starting point. It will then learn much more like a human being – with the beliefs and values that those humans gave it.

When many people in a society share a similar graph, interpreting the world in a similar way, that society organises itself around the structure of their graph – and the values, beliefs and desires that it expresses. This is how the causal graph structure scales up to guide the economy, and the politics, of the larger world. The graph starts as our way of interpreting the world, but it ends up as our way of shaping it.

Agent-based modelling, also mentioned by Cummings, is a method for understanding how that happens. The 'agents' are computer simulations of human beings. The computer creates thousands or millions of agents, and imagines what would happen if they all interacted with each other. If each agent has its own causal graph inside its simulated mind, we can look at how those graphs evolve, how they spread from one person to another – and which techniques are most persuasive at changing their minds.

These agent-based testbeds – as I discuss in this talk – are powerful ways to find solutions to tough social problems. But it all depends on the values they start with. In Euristica, the simulated world I developed, the objective is to identify the causes of inequality and discrimination, and find solutions to them. Someone else might build an agent-based model with a different set of values.

Altogether, this collection of ideas provides a powerful set of insights into human cognition, the structure of society, and technologies that can simulate and run experiments using powerful AI. I will continue to use them to seek new solutions to the social challenges that are holding humanity back: from discrimination and division, to productivity and poverty. In the meantime, if Dom wants to chat, I'm right here.
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Other writers on System 3
Leigh Caldwell
cognitive economicsmarket researchsystem 3
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Since last year I have been discussing the idea of "System 3" - a set of mental capabilities and processes involved in imagination and mental simulation. These capabilities appear to be used for several mental activities, notably:

  • planning and thinking about the future
  • counterfactual reasoning
  • daydreaming and mind-wandering
  • consumption of fiction
  • mental replay of past experiences
  • and in empathetically considering how other people experience an event


Recently some other writers in the market research industry and elsewhere have been discussing System 3. Here are some of their thoughts:
System 3 has also been mentioned in a couple of recent books: Betty Adamou's Games and Gamification in Market Research, and Richard Chataway's forthcoming The Behaviour Business.
So the industry is enthusiastically taking up System 3. The science is progressing too - I will post separately on some of the latest psychology and neuroscience research that relates to this topic.
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Book review: Alchemy, by Rory Sutherland
Leigh Caldwell
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Rory Sutherland's new book Alchemy: The Surprising Power of Ideas that Don't Make Sense continues his 10-year campaign against the traditional, logical pursuit of business advantage, through a scientific lens that includes several cognitive economics themes. As ever, a curated series of amusing anecdotes about people or companies who took an unusual angle on marketing or product invention, fuel a philosophical wander.

That philosophy could be summarised as: if it makes sense, someone's already tried it. So try something that doesn't.

The ideas that underpin the book are broadly based on behavioural economics and cognitive science, with bits of evolutionary theory, statistics and old-fashioned advertising intuition thrown in. At first it doesn't look like a behavioural science book as such: the theoretical backbone takes a while to show. Rory's style is discursive: an after-dinner-talk of anecdotes, dismantling of conventional wisdom, ever-so-slightly outrageous assertions, and the periodic emergence of abstract wisdom in the third paragraph of a mid-chapter page.

Some of those smart insights include:
  • logical "engineering" solutions often do work, but given that nearly every problem in the world has had someone try to solve it with logic, the remaining unsolved ones are those where logic failed. So if you are working on a problem that still exists, try something illogical. (This is, perhaps, the generalised version of "look for where there are no bullet holes".)
  • people tend to focus on optimising the obvious features of a product: you can often find smart new solutions by shifting to a less obvious dimension. For instance, make a vacuum cleaner look like a cool piece of tech instead of just cheaper or stronger. If you can't make your plane take off on time, it may be almost as good to give reliable information about how late it will be.
  • it's useful to have a space, such as your advertising agency, where it's OK to ask silly questions and challenge long-established premises.
  • what people do with their money is often a better guide to their desires than what they say.
Many of the examples will be familiar to readers of Rory's earlier books, articles, talks or tweets - I'm sure I've heard twenty times about the value of making train journeys twenty percent more fun. But no harm in this - there are only so many good anecdotes out there, and why not squeeze maximum efficiency out of them. The CFO of Rory's brain would no doubt approve.

After an introductory section, the deeper scientific ideas behind the book start to emerge. Five concepts inspire a new set of marketing approaches: insights from statistical mathematics (ergodicity), cognitive economics (changing the value of things by changing the mental state through which they are interpreted), evolutionary theory (signalling and self-signalling), cognitive science (satisficing), and perceptual psychology (psychophysics).

This is where Rory's approach diverges from the typical ad guru. It's a complex-systems philosophy: the idea that systems of different kinds often exhibit similar structural elements or dynamic phenomena. Thus, a strategy that works for flowers might also work for brands. A statistical insight from the criminal justice system may also have value in planning public transport investments, or the pricing of airline tickets.
There is a strand of politics running through the book that emerges from Rory's iconoclastic style but will grate with some readers. While Rory is in many ways a liberal thinker, he sees liberal-centrist politics as too conventional and technocratic, and enjoys puncturing mainstream assumptions. Yet when he points out the failures of Hillary Clinton's logical, data-driven campaign, he ascribes too much likelihood to the success of Donald Trump's alternative approach. Yes, Trump is unpredictable, and this could in principle be a useful tactic in trade negotiations, but so far it hasn't achieved much.

I still think Trump won the election mainly due to a factor that's acknowledged, but not emphasised, in this book: luck (though he did happen on a messaging and emotional strategy that at least gave him a chance of competing). Most theories, and most books like this, are an attempt to explain the real reasons behind things, to provide meaning and salve the anxiety of not-knowing. Only a few (such as the early books of Nassim Taleb, whom Rory acknowledges here) admit that a lot of the world just can't fully be predicted or controlled.
I didn't agree with a couple of details in the book. The first chart in the book categorises a set of ideas that "work" and "fail". "identity politics" appears on the "fail" side. I'm not sure why this is supposed to have failed, especially as a few pages later, he relates two examples of identity politics that succeeded wildly: the election of Trump and the Brexit referendum. Perhaps it only counts as "identity politics" when it talks about racial justice, pronouns and gender, not when it makes salient an identity dimension of working-class, anti-elitist "real Americans", or faux-Churchillian anti-Europeanism? In any case, this kind of detail may put off a readership segment who don't feel their identities fit the mould of the (still mostly white, male) ad guru.
Second quibble: it's an oversimplification to say that universal scientific theories pay no attention to context. Physicists would dispute this: the laws of motion take an abstract form that is very well structured to allow for the context of friction, varying gravitational forces or other environmental factors. The real argument is not against the application of science but its misapplication. Science misses the mark not when it goes too far, but when it doesn't go far enough. It's true that economists and psychologists haven't spent enough attention on modelling context and how it can be described - this is an important topic that cognitive economics could help explore. This does leave an opportunity for those who think about it from a creative point of view instead.

A good scientist, or even a good accountant, does not claim that their models explain everything or are perfectly right. They acknowledge the uncertainty and noise of the real world. A high-quality social science theory might explain 60% of the variance in observed outcomes, and that 60% gives us some powerful conventional tools to make an impact in business or policy. The remaining 40% leaves plenty of space to try something outside of the model, and this is where Rory suggests we apply alchemy.
This book will both entertain you and give you some inspiration to find new alchemic ideas in that 40% space. Most likely, two-thirds of those ideas still won't work - that's certainly what I've found in my own research practice - but if you keep thinking, imagining and testing, that last third will help you find some breakthroughs.
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From Behavioral to Cognitive Pricing
TaraECooper
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Below is an article by Leigh published in INsights magazine. The magazine published by the Neuromarketing Science & Business Association. See the article in its full glory here or just the text below.

Behavioral pricing has been used for many years but is essentially based on changing customers' behavior without creating new value. Cognitive pricing is a new paradigm in which the customer's positive mental experience can be given a monetary value.

The ability of companies to earn premium prices for their products and services is under threat. The rise of the Internet in general, and price comparison websites in particular, makes it easier for consumers to compare products, harder for brands to stand out from the competition, and risks turning many categories of product into commodities. And being commoditized usually means lower profits and less innovation. The emergence of behavioral economics gave marketers a new set of tools to maintain an edge: the techniques of behavioral pricing [1], [2]. This toolbox included leveraging judgment heuristics like anchoring, Goldilocks pricing, the left-digit effect, decoy effect and many other clever tricks. Most of these operate by influencing how customers perceive or estimate the monetary value of the products they buy. Anchoring, for example, can be used to compare the price of what you're buying with some higher reference price – so that you are willing to pay more. Behavioral pricing still works well, but it is becoming less powerful than in the past.

In some areas, regulators are limiting the scope of the techniques that can be used. The UK's Financial Conduct Authority is acting to stop insurance companies and others from using dual pricing – offering their services cheaper to new customers than to customers who renew for a second or third year. The Competition and Markets Authority already limits the use of reference pricing by retailers. And the European Commission regulates how prices of travel tickets are presented to consumers, for example insisting that all taxes are included and no mandatory surcharges are added.

In other areas, intermediaries make it harder to apply behavioral techniques. A supermarket might produce labels showing the price per 100ml or price per kg of all products, making it easy for consumers to compare the deal they are getting from competitive brands. And price comparison sites usually rank products from cheapest to dearest, making some techniques like Goldilocks pricing less successful.

However, two basic rules of economics still apply even today:
  1. customers are willing to pay a price for a product that reflects the value it gives them, not just the underlying cost (although of course they are happy to pay less if they can get away with it).
     
  2. if a company can apportion its fixed costs differently across different customers – for example, charging rich business people more for a last-minute plane ticket than careful tourists who book in advance – they will use resources more efficiently, and satisfy more customers, than if they charge the same price to everyone.
A world of price comparison and commoditized pricing risks destroying the business model of some companies. Although consumers might feel unhappy about some of the prices they pay today, they would be worse off if Ryanair, France Telecom and AXA Insurance went out of business altogether.

Recent discoveries in neuroscience research are at the basis of a new approach that better aligns the interests of consumers and companies: cognitive pricing. The basis of cognitive pricing is the insight that people gain value from their own state of mind. My beliefs, my mood, my identity and self-image, my affiliation with organizations, the stories I tell myself, my dreams and plans for the future – all of these can make me happier and provide real value to my life.

None of my beliefs, values, or aspirations are material products or services that I can acquire, but they do matter to me – and I would be willing to pay for them, at least indirectly. These mental states are studied by the newly emerging field of cognitive economics, and they are called cognitive goods.

Cognitive pricing is a method for packaging up these cognitive goods along with the products and services we buy. The cognitive goods themselves provide extra value that customers are willing to pay for – and they differentiate the products from competitors, so that the product is less at risk of being commoditized.

Traditional "rational" economic decisions based on price are made primarily in the prefrontal cortex using "System 2" reasoning, which is good at following mathematical rules. Behavioral pricing mainly operates at the "System 1" level, accessing the limbic and sensory brain's capacities for pattern recognition, pairwise comparison and emotion. But decisions based on cognitive pricing, based on valuing complex mental objects, do not fit comfortably into either “System 1” or “System 2.” Some authors [3], [4], [5], [6] have proposed that there might be a third mental system, representing a different level of cognition. This level might represent the human imagination, found to take place mainly in the striata and default mode network, and/or a process in the vmPFC and anterior cingulate cortex that monitors and controls the other two systems.

There is some tradition in both psychology and economics of understanding how people consume and benefit from intangible value. Thomas Schelling's 1984 article, "The Mind as a Consuming Organ" [7], explored how we gain pleasure from fictional objects, and Ainslie's Picoeconomics [8] (1992) followed this up with a model of "self-reward". Rory Sutherland's classic TED talk [9] has some excellent and amusing examples from the commercial world; Ariely and Norton (2009) developed a theory of conceptual consumption [10] and Kimball explored cognitive economics in a 2015 article [11]. The practice of cognitive pricing builds on these insights, to show companies how to capture some of that intangible value by putting a price on it.

Examples of cognitive pricing:

Coca-Cola
A can of Coca-Cola bundles together a lot of different benefits – some tangible and material, others only inside the consumer's head. All of the material benefits – with the possible exception of taste – can be provided easily by other, cheap alternatives. The cognitive goods, however, are mostly unique to Coke. These goods are what enables Coca-Cola to charge a premium.

British Airways (or KLM, Lufthansa or Air France)
Think specifically about the short-haul services of these airlines. The material experience is very similar to that of a budget airline: the seats are a similar size, the flight takes the same length of time, and (in economy class) you don't even get free food. One of the only concrete benefits may be the chance to fly from a more convenient airport, though even that isn't always the case. Yet many people are willing to pay much more for a British Airways flight even when a cheaper Easyjet flight is available.

Why?
The cognitive benefits of a British Airways trip explain this. They include:
  • The implicit association with the airline's long-haul offerings: the dream of travel to an exotic location, in flatbed luxury, is still somehow in the air on your one hour flight to Munich for a meeting.
  • The status game: you collect tier points or miles towards the elusive Silver or Gold status that will set you apart, psychologically as well as physically, from the rest of the passengers
  • The gamble of an upgrade: you always have a chance of an upgrade to business class. Even though on a European flight that still doesn't provide much extra material benefit, it feels special.
These benefits are often wrapped up in the general label "brand", but there is a lot more to learn by analyzing the specific differences between the two offerings. You can apply psychology or neuroscience research to understand the additional value each component might attract. The "upgrade lottery" can be valued using decision-making experiments on probability based gambles, and the worth of the implicit "long-haul luxury" association can be measured with implicit market research tools.

Good Energy
This is an energy company that provides electricity to the consumer's house, in exactly the same form, voltage, etc. as any other company, but commits to generate it only from renewable solar sources.

The electricity is the same as any other, but by buying from Good Energy you get to believe in a positive story about yourself and the future of the environment. The word "story" is a tricky one in this context: I do not mean to imply that the story is untrue. Most probably it is an accurate story. But the direct material impact your purchase makes on the environment is unmeasurable in terms of your own personal future. In reality, it is your participation in a mass movement of individuals that has
a chance of making a difference, and that participation is yet another cognitive good that you are buying with your energy contract.

How much are cognitive goods worth?
We might enjoy the story or belief that comes bundled with a product, but does it really influence the price? Perhaps it helps sway the decision between two equally-priced cans of cola or electricity providers, but can it really earn a price premium?

The general answer is yes. Coca-Cola is typically priced at two to five times what a supermarket cola costs. British Airways tickets are often 50-100% more than Easyjet for the same cities and dates. Green electricity deals don't always cost more, but this is partly because they have benefitted from government subsidies on renewable energy generation.

Not all suppliers have successfully created a story that can earn a premium like Coca-Cola’s, but the possibility is always there. In some categories the cognitive premium will be smaller than in others, because of the market structure or the nature of the decision-making context. But in most industries there are great opportunities.

In most categories there is room for a more environmentally friendly option, a more "authentic" product (or several, depending on the story you tell about authenticity), a version that is targeted at a younger audience, and many others. Each of these is associated with a cognitive good that you plant inside the customer's mind. When they buy or use your product, that cognitive good is activated and replayed, creating more reward and pleasure for the customer.

The best way to find the cognitive goods associated with your product or brand is to identify the stories that are available in your category, and those that are not yet being told by your competitors. You can do this by exploring your customer's imagination – their "System 3" – measuring how much cognitive reward is generated by their different beliefs, identities and dreams. Then, start telling those stories – and you'll find out how much more your customers will pay to play a part in them.


References
  1. Liu, M. W., & Soman, D. (2012). Behavioral pricing. In Handbook of consumer psychology (pp. 656-678). Psychology Press.
  2. Caldwell, L. (2012). Psychology of Price: How to use price to increase demand, profit and customer satisfaction. Crimson Publishing.
  3. Stanovich, K. E. (2009) "Distinguishing the reflective, algorithmic and autonomous minds: is it time for a triprocess theory?" in Evans & Frankish In two minds: Dual processes and beyond (2019), Oxford University Press.
  4. Evans, J. St B. T. Evans (2009) "How many dual-process theories do we need? One, two, or many?" in Evans & Frankish In two minds: Dual processes and beyond (2019), Oxford University Press.
  5. Houdé, Olivier (2019) 3-System Theory of the Cognitive Brain, Routledge.
  6. Caldwell, L. (2018) "Introducing System 3: How we use our imagination to make choices", GreenBook. https://greenbookblog.org/2018/04/24/introducing-system-3-how-we-use-our-imagination-to-make-choices/
  7.  Schelling, T. C. (1987). The mind as a consuming organ. The multiple self, 177-96.
  8. Ainslie, G. (1992). Picoeconomics: The strategic interaction of successive motivational states within the person. Cambridge University Press.
  9. https://www.ted.com/talks/rory_sutherland_life_ lessons_from_an_ad_man?language=en
  10. Ariely, D., & Norton, M. I. (2009). Conceptual consumption. Annual review of psychology, 60, 475-499.
  11. Kimball, M. (2015). Cognitive economics. The Japanese Economic Review, 66(2), 167-181.
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What is cognitive economics?
Leigh Caldwell
beliefscognitive economicsmental states
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What's happening inside your head right now? What thoughts, feelings, ideas are spinning around in there? Are they important to you? If you were not able to think those thoughts, would you care? How much does your internal mental experience matter to you?

To an economist: not at all. Traditional economics explicitly rules out any consideration of how people think, and what is going on in their minds or hearts. Economists only trust what they can observe: specifically, the things you buy and sell. This can include selling your labour (for a wage) and buying and selling services, but in practice it mostly means the physical goods that we buy and consume.

Yet most of us know there is more to life than buying and selling. The activities inside our heads are a major – maybe the major – contributor to our quality of life. Are you happy? Do you have purpose in your life and work? Do you feel appreciated? Are you looking forward to the future or anxious about it?

Our state of mind is of great importance to us, but economists have mostly ignored it and focused only on the material goods that they can see. Even behavioural economics, which in the last few decades has explored some of the psychological processes behind economic decisions, still deals in money and physical goods – not emotions, hopes, beliefs, fears and ideas.

Cognitive economics is a new field that takes the inside of the mind seriously. It treats all of these mental states, beliefs and emotions as what they are – things people really want – and asks economic questions like: what are we willing to give up to get them? How do we manufacture and distribute them? How much are they worth? And the biggest economic question of all: how can society deliver all of us the best possible outcomes? Not just in terms of material wealth, but of our own mental states.

To see the importance of cognitive economics, look at how you spent the last hour. Taking myself as an example, I have participated in four traditionally "economic" transactions: I made a cup of instant coffee, used my computer (purchased three years ago), sat in my house (which I rent), and put some plates in the dishwasher. None are significant in the economic scheme of things – together, they contribute about £1.90 to British GDP. But what did I do inside my mind?

  • I thought about the ideas behind this article
  • I imagined my readers and what they might want to see
  • I considered aspects of my relationships with colleagues
  • I attempted not to think about the result of the Europa League final last night (with partial success)
  • I was distracted by notifications on my phone
  • I replayed the ending of Game of Thrones in my head
  • I looked at the view of London outside my window
  • I sent some chat messages to a co-worker
  • I imagined what it might be like at the conference I'm missing in Italy
  • I started writing a thank-you note to the hosts of last week's book group
  • I made a mental todo list for a few things I need to remember later
  • I looked forward to next month's holiday in Peru


The list goes on. All of these activities are just as important to my experience of life as any traditional economic transaction. It's true that I need to meet my basic needs – food, shelter etc – in order to have the space to indulge in mental flights of fancy. But once those needs are met, most of us spend only a small proportion of our time and resources on basic survival. Even if you're in a repetitive job earning just enough money to live, your mind can still be off doing other, greater things. The cognitive economy in our heads gets most of our attention, dominates daily life and deserves analysis and understanding.

How a mental state comes about is an important topic of cognitive economics. Sometimes it is a side-effect of an activity like working or eating. Other times we acquire it deliberately by engaging in the media or other forms of communication. Often it is internally self-generated as our mind wanders – but even then, it is a consequence of a previous action, thought or learning experience, which we can analyse. In a sense we can trade mental states – not directly, but by speaking and listening to other people and being willing to absorb their ideas and views.

If we think beyond what's visible, the economy has always been about states of mind. I get half the pleasure of my holiday from anticipating it before it happens. I get much of the pleasure of a film from remembering it afterwards.

Physical objects are like this too. Do I buy a car only in order to have a car? No: I buy it to get from A to B: that is, to gain access to sensory experiences that are further away than walking distance. It may give me extra benefits too: a feeling of status, the pleasure I take in its aesthetic qualities, my affiliation with the brand or the satisfaction of its sustainable electric engine. All of these benefits accrue inside my head, not outside.

So even within the domain of material goods, today's economics is missing a part of the exchange. Everything I do is about managing my subjective, internal experience of the world. Objects are just a means to an end.

Think of the Buddhist monk, able to control their feelings and state of mind by will and disciplined practice, devoid of material possessions. That monk's life may seem strange, but they are not that different from you and me. We all seek to optimise and control our thoughts and feelings. In the old economy, we did it with cars, money, alcohol and perhaps religion. In tomorrow's cognitive economy, we might learn how to do it with digital experiences, language, relationships and by building, in our imagination, the mental worlds we most want to live in.

I believe the future only points in one direction: to a more cognitive, less physical economy. Digital technology and psychological science will enable it, and the environmental constraints of the world demand it.

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Why endings matter [spoiler-free Game of Thrones references]
Leigh Caldwell
cognitive economicscognitive goodsfictionnarrativesstoryTV
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It probably has not escaped your notice that the Game of Thrones TV series finished this week. If you use social media at all, I suspect you also saw some anguished squawks about how awful the ending was. How the incompetent writers screwed it all up. Maybe you even signed the petition, along with 1.5 million others, to have the last series remade with a different conclusion.

Personally I thought it wasn't a bad outcome, but I seem to be in the minority. Either way, why does this have such significance?

I read a counterargument a few days ago: You've had 70 hours of enjoyment already – it's in the bank. You enjoyed episode 1, 2, 3, …and you can't go back and "unenjoy" them now. One bad hour at the end can't rewind the clock and eliminate the last 8 years of pleasure?

Yet this feels wrong. The ending can ruin the beginning. Why should this be?

A model from cognitive economics may have the answer. The theory of "cognitive goods" says that watching TV, or reading books, is not a one-off consumption experience. It's not like having a massage or eating dinner – something that you enjoy it while it lasts, that might have some short-term after-effects, but is basically over when it's over.

Instead, watching TV is more like building a house. While watching, you are constructing a world inside your head. You learn about the characters and create representations of them in your mind. You watch their behaviours, infer personality traits and record them in a consistent, structured mental model that tells you how they interact and relate to each other. That model stays with you. It sits in your head, occupying space and attention, and you can revisit it regularly to inspect it, or wander through and enjoy it.

You can use this model to try out hypotheses: what might happen next? What if Jaime goes back to Casterly Rock? What if Dany doesn't go north to Winterfell? What if so-and-so isn't dead after all, or such-and-such was evil (or good) all along?

You can play out in your head an imaginary version of Arya's adventures, or what Tyrion might do and say next. Half the fun of watching an episode is the continuing speculation about how it will turn out, making up theories and being proven right or wrong.

In economic terms, you have invested in an asset – albeit one that only exists inside your head. And it generates an ongoing stream of consumption benefits, every time you think about it, imagine this fictional world and feel the emotions, excitement and anticipation that it creates.

In psychological terms, it sits in what I call "System 3", your mental capability for imagination and mental simulation. Your mind contains a map of all the objects in Westeros and their relationships to each other, and you can use it to make predictions or inferences about them. The act of running these simulations generates synthetic reward – reward that is self-generated instead of coming from the material world outside your body.

And the nature of this mental construction tells us why the ending matters. The house you have built in your mind is waiting for its capstone, its roof, its front door. If the final touches are badly done, the whole house suddenly looks wrong. You won't enjoy wandering through it any more. The 70 hours you have invested in building this castle really could be wasted.

If you really did build a house you might very well enjoy the process. There could be a satisfaction in putting one stone on top of the next, and picking out the window frames, and seeing it rise, floor by floor and room by room, out of nothing. But, unless you are a monk building a sand mandala, you are not just constructing the house for the pleasure of creation. You also want to end up getting a house out of it.

Until the house is finished, its structure is not secure. If the keystone is wrongly placed, the whole thing can fall down – or become an inconsistent, badly founded, broken ruin of a building. It will no longer be fun to walk around this imperfect world, because you no longer trust it to make sense. It doesn't provide pleasure any more because the satisfying logical chains of cause-and-effect have been broken. Causality and a consistent set of logical implications, or at least something close enough to logic that you can persuade yourself to believe in, are a requirement for your cognitive asset to generate reliable mental reward.

A lot of our activities, especially our interactions with media, or information, or in our relationships with people, are about creating something inside our heads that lasts beyond the interaction itself. Have you had a relationship that you enjoyed in the moment, but was ruined when you found out the person had been lying to you? Your pleasure was corrupted even after you'd finishing having it. It's the same phenomenon.

Every experience you have has two dimensions: the feelings you have at the time, and what you teach yourself as you interpret them. When you create a mental model of a world – whether it's Westeros or Earth – you own something valuable. Anyone else, whether the writers of Game of Thrones, or your cheating boyfriend, messes with its foundations at their peril.
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What makes a useful theory?
Leigh Caldwell
behavioural economicscognitive economicsdecision making modelsmodellingrationalitytheory
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If conventional economic theory is so wrong (as we are repeatedly told) why does it survive so well?

This post by UnlearningEcon prompted me to think again about why economics, despite widely accepted empirical data from behavioural econ, is broadly taught in the same way as before, and why its basic assumptions still underpin much modern research.

Some have a sociological explanation for this. In this view, economists are invested in the old approaches, have spent decades honing specific mathematical skills, and effectively collude to make sure new ideas do not displace the old. The top journals only accept papers that cite the same old work, perpetuating the models. Science, as they say, advances one funeral at a time. No doubt there's something to this, but I don't think economists are quite so closed minded.

There is a clue in the above article:
"...Euclidean geometry, despite being incorrect, is more effective than non-Euclidean geometry in some engineering and architecture."
In practice, it's hard to see why an engineer or architect would even think of using non-Euclidean geometry - unless they are calculating a space probe's route to Neptune (or in a few specific scenarios relating to aeroplane flight paths). In earth-bound contexts, Euclidean geometry is so close to being right that there's no point bringing in non-Euclidean approaches.

UnlearningEcon complains about the use of "as if" theories: when a theory has been empirically disproved by data, why do we keep pretending it's true? Why model people as if they are rational utilitarians when they're clearly not?

A theory is useful because it enables us to predict - and therefore control - the world. Two properties make a theory good at this job:

  1. It should accurately reflect the world
  2. It should be practically generalisable
The most solid, widely-used theories do well on both criteria: the laws of thermodynamics or statistics are highly accurate (no better model has been found) and they can be expressed in relatively simple, generic forms that can apply in many contexts.
Some theories, like Newtonian mechanics, are known to be slightly inaccurate in some contexts (Einstein's relativity superseded them) but are still so close to being right - typically within a millionth of a percent - that it hardly matters. And because Newton's laws are so much easier to generalise and use than Einstein's, it's a more useful framework.
Now, economics. We do know that the rules of economics - utility maximisation, the irrelevance axiom, etc - are wrong in lots of real-world contexts. They aren't so wrong as to be useless, but they are far less accurate than Newton's laws. UnlearningEcon mentions the priority heuristic, which does make better predictions of people's behaviour in lotteries than its competitors, expected utility or expected value theory.
But the priority heuristic is hard to generalise. Knowing how people prioritise gains and probabilities in a formally-specified gamble does not tell us how they prioritise money versus pleasure, or more leisure versus a bigger TV. It doesn't say much about how producers will choose what products to offer, or about the larger picture of a market or a whole society.
Standard utility and price theory, on the other hand, is very general. Theories relying on those underpinnings can be applied to any market, and to a lesser extent to non-market contexts (there are lots of theories about the economics of crime or marriage, for instance).
If you're looking for a way to predict what happens in a life situation, of course you'd rather have an accurate, empirically-supported theory to tell you, based on data, what to expect. But if you don't have one, because the data hasn't been collected yet in your specific situation, a second best approach is to rely on a generalising a theory from other scenarios.
Classical economics lets you do that. You can borrow rules that work reasonably well, apply them in your context and have a ready-made set of tools and predictions that have a chance of being roughly right. Maybe nobody has gathered enough data to make a theory specific to the crab fishing market off the Isle of Mull, but the theory tested in the trading pits of the Chicago commodity markets reads across close enough.
More realistic economic theories have two ways of emerging, then.
First, we can keep collecting more situation-specific data and create theories for each context. Then we can independently develop a Mull Fishing theory, a theory of social media clickthrough rates, a theory of how people use shared resources. It will be a lot of work, but the predictions will be well calibrated to their environments and probably quite accurate.
Second, we can work on generalising the psychological and behavioural theories that have been developed in narrower domains. I think this is the direction that holds out hope for better economics.
I anticipate that behind the priority heuristic lies a deeper explanation, that can tell us why people make choices based on minimum gain, and why probability is more important than maximum profit. The Vlaev et al paper linked from UE's article reviews examples of work that points in that direction, but it is not a unified theory in itself.
Some behavioural economists disagree that a unified theory will be possible, while others see it as the next important step in the field's development. I'm with the second group, but regardless: we don't quite have one yet.
In the meantime, it's inevitable that economics practitioners and teachers will rely on the one general theory they have: neoclassical utility maximisation. And, in 80% of the practical decisions they must make, they will be right to do so.
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Reporting back: the Cognitive Economics session at the AEA conference
Leigh Caldwell
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[Tara posting today]

We had a great response to our Cognitive Economics session at the American Economic Association’s conference a few weeks ago.

For those who weren’t there, here’s a quick summary of the four papers presented, and the discussion at the end.

Dan Benjamin, Kristen Cooper, Ori Heffetz, and Miles Kimball presented What Do People Want?

The key question of this paper is: how can we measure happiness? And how do we account for the biases and differences across different groups and demographics?

The authors have built a model that examines the multiple dimensions of wellbeing by asking individuals a huge number of different questions. Any single question about your true wellbeing will be affected by a lot of “noise” - i.e. biases. They reduced this noise by asking individuals multiple questions including tradeoffs and interpersonal comparisons.

By statistical analysis, they found a small number of underlying factors that best predict the different dimensions of wellbeing.

The authors’ broader claim is that use of survey questions to measure subjective wellbeing is an effective method and currently underutilized by economists. In the future, they believe more data of this kind will provide more accurate measures of wellbeing, and offer policymakers better levers to improve citizen happiness.

David Hagmann and George Loewenstein presented a paper on Persuasion with Motivated Beliefs.

David and George’s paper focused on how one can, and whether one should, lower other people’s psychological defences, and explained their ongoing experiments to test these questions. In general individuals have “asymmetric updating”: we listen to information and update our beliefs only if this information supports those beliefs. We find it easy to ignore information conflicting with our existing assumptions. David models this as a two-stage understanding of persuasion. Stage one is asssessing whether new information will threaten your beliefs and stage two is collecting the new information and updating existing beliefs (in a biased way, based on the degree of threat). As such, persuaders can be regarded as a potential threat: are potential persuaders going to challenge your views? Do these persuaders have the necessary expertise to challenge or comment on your beliefs? And also, how invested in this specific belief are you to be/to not be persuaded from it?

All of these questions fed into the testing model that David and George have been working on. They have built an experimental model that aims to see if messages coming from a more likeable sender, or someone who appears less certain about their opinions, reduces the defences of those receiving the message - so that the message will be more persuasive. . “Receivers” were asked to indicate agreement with certain political views (e.g. “Welfare benefits should be increased/decreased”) , and also to estimate the answer to associated questions (e.g. “What proportion of food stamp recipients have a job?”) . “Persuaders” were then incentivised to change the opinions of the receivers. Persuaders were encouraged to get receivers to acknowledge alternative views, show doubt and/or to build a rapport with the “Receiver” to see if that helped them change their mind.

The findings of the research include:
  • A person who expresses uncertainty in their beliefs is more persuasive - but only if the receiver cares about the topic, and therefore has stronger defences against new information
  • Likeable people are not (significantly) more persuasive
  • Highly persuasive people also ended up persuading themselves of their own arguments

Emily Ho, David Hagmann and George Loewenstein presented a paper on Measuring Information Preferences.

Emily presented a fascinating paper on how individuals prefer to receive and/or avoid information and how one can measure such information preferences. The paper starts from the (rational) premise that everyone should be looking to gain more and more information as it will help us to make better decisions. In healthcare, this may mean getting testing for specific diseases, or in your social life, this may be knowing how well you performed in a certain social situation. However, as you may instinctively feel, this was not the case when surveying groups and individuals. For instance, when hypothetically asking individuals if they wanted to know the results of a test about whether they had a genetically inherited disease, 44% of people claimed they “probably didn’t want to know” or “definitely didn’t want to know” the results.

The authors went on to ask a variety of audiences from caregivers to listeners of scientific podcasts about their information preferences, and, from these surveys, they have created the Information Preferences Scale (IPS). Emily confirmed that the IPS is psychometrically and behaviourally validated for measuring individual level information preferences. The paper concludes that the IPS can predict information acquisition in hypothetical and real life tasks and scenarios. Naturally, it is easy to see how these kinds of information preferences can have some clear implications. How can one disclose information successfully if certain people or groups are not going to be receptive to such information? From individuals making better healthcare decisions to governments improving public awareness campaigns, this research has some promising real world applications.

Leigh presented a paper on Cognitive Goods, Normal Goods and the Market for Information.

Leigh’s paper starts from the premise that there are things we care about that don’t seem to fit the rules of economics. Whether it’s the fate of Lassie in the 60s TV show, or the fact that the person in the next cubicle earns less than I do, or the results of my favourite sports team or political party, these beliefs or mental states have value to me, but that value can’t be measured or priced using economic analysis.

If we are to understand the growing role these kinds of objects play in the modern economy, we need to define the economic rules they play by. Leigh set out half a dozen proposed axioms for “cognitive goods”, the mental objects that play an important role in our experience of the world but that can’t be acquired and traded like traditional material goods.

He proposed a mechanism that may operate in the mind to put value on cognitive goods, how they are used in decision making and when we mentally simulate future or hypothetical events, and empathise with other people (see the recent System 3 blog post for more details).

The goal of this theoretical work is to provide a unified view for some of the mental phenomena the other presenters have discussed; and to illuminate how beliefs, symbolic value and processes like advertising create much of the value in today’s “dematerialised” economy.


After the presentations, we had a short panel discussion between Leigh, Emily, Miles Kimball and George Loewenstein plus audience questions. A tricky question was whether “cognitive economics” is the best term for this field. Although it is clear there is a growing dissatisfaction with traditional economic models (especially in the information age), the term cognitive economics may be too cerebral. Some worry that the term negates the mental states, emotions and beliefs that form the core focus of research in this field. Nevertheless, the panel also drew attention to how this work can have some promising real-world applications. For instance, as accurate happiness measures are created, this scales could hold governments to account on their attention to citizens’ wellbeing priorities.

The session finished with two calls to action. Firstly, for academics and researchers to pursue small and well defined studies that can then be used as building blocks to create the bigger picture in this field. The second was to join Leigh and any other interested academics at the upcoming Cognitive Economics workshops. There will be two in 2019 - one in June in London and one later in the year in North America (perhaps a great chance to develop or present some of these small building blocks?!). We are planning on announcing the dates and details on how you can get involved very soon!

If any readers are interested in participating in a similar session at next year’s AEA (in San Diego - a highly recommended location for a January conference) please get in touch. We will be submitting the proposal in mid-April.
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Three systems: a mechanism for mental and social narrative
Leigh Caldwell
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Alex Rosenberg says here that we are instinctively driven by stories, narrative and theory of mind - a very useful instinct on the small scale - although that instinct can be misleading on the larger scale of history and politics. His book on this claim is also out, though I haven't read it yet.

It seems uncontroversial that the idea of narrative has a powerful hold on how we think. There are thousands of discussions of storytelling as a way for us to bond with other people, and the biases that come from our desire to see a natural story behind events. I don't think many would disagree that stories are compelling to most people, and that we naturally like to see the world through narrative.

I've been exploring how the mind might implement this, and what the consequences might be.

Readers may recall the System 3 theory from earlier posts. This is how I think narratives fit into that, and how the process works in the mind:


  • People (and other animals) are very good at learning cause-effect relationships. These relationships are binary pairs, A->B, and encode the knowledge that when you see A, B is likely to follow soon after.

    This could be expressed as "A causes B", "A predicts B" or "A implies B" - there are important logical distinctions between the three, but I suspect the intuitive mind isn't very interested in those differences - it just learns the relationship. This way of encoding knowledge about the world underpins psychological basics like behavioural conditioning and associative learning.

    Indeed, this kind of knowledge is necessary for any organism to function in the world. Whether it's a single-celled amoeba floating towards water with more concentrated nutrients, or a flower turning to the sun, there is a simple relationship between stimulus and action. Simpler organisms have these relationships coded into their genes, while more complex ones can learn new relationships from their environment.

    These relationships make up what is typically called System 1. The automatic, instinctive reactions - touch a hot cooker and jump back, or see a car's brake lights and hit your own brakes.
  • The big leap comes when an organism is able to assemble these relationships, these binary pairs, into chains. If A->B and B->C, then A->B->C - and, cutting out the middleman, A->C. If a sparrow in the sky implies that a worm is in danger of being eaten, and daylight leads to sparrows in the sky, the worm might learn that daylight implies getting eaten. The early worm, as they say, gets caught by the bird. This longer chain of implications enables the organism to plan, act earlier, and gain an advantage.

    The implication chains used by humans are usually much longer and more complex, of course. Getting up this morning -> being able to go to work -> not letting down my clients and colleagues -> getting paid -> having enough money to buy food and pay rent -> not starving. So, getting out of bed becomes rewarding despite the discomfort involved.

    Human chains also include branching and uncertainty. I might get paid despite not turning up at work, or I might not. I may have enough money to feed myself next month anyway. There may even be cycles when the chain points back to itself: being fed next month makes me more likely to feel like getting out of bed again. The chain in these cases branch out and become trees, or more complex graphs such as the examples shown below.

    These graphs - I referred to them as causal graphs previously, but I now think implication graph is a better name - are the territory on which the human imagination plays out. When we plan the future, or imagine the outcomes of a decision, we are navigating the implication graph. Our brains are highly tuned for this: they can do it quickly, and efficiently evaluate the outcomes. If navigating a particular section of the graph feels good, the events they represent would probably feel good (be rewarding) in reality too. This is what I propose to call System 3.
  • Something that, as far as we know, we can do but worms can't, is think about the implication graph for other people. It is important when interacting socially with other humans to be able to predict what they will do in different situations. This means we have to have an idea of what implication graphs might be inside their heads. It seems natural that the very efficient brain mechanism for evaluating our own implication graph would be reused to evaluate other people's. Why evolve two separate functions when one will do? (There is also good neuroscience evidence that this is exactly what happens.)

    As Rosenberg points out, this capability is incredibly useful on the level of individual short-term interactions, and rarely leads us too far astray. Whether I'm exchanging my fruit for your bread, cooperating with you to build a barn, or taking care of a child together, my basic understanding of your goals, behaviours and incentives do very well at helping me operate correctly. There is plenty of opportunity to get feedback and adjust my assumptions if they're wrong, and I probably know enough about you to have a reasonably accurate copy of your map in the first place.

    Notably, if my brain is missing any pieces of your implication graph, it can fill them in with copies of my own. If you've never explicitly told me that you love your children and worry about their safety, I can probably assume you're similar to me in this respect. You might not have mentioned your enjoyment of eating cakes, but if I like it, you probably do too. This 'copying' heuristic is likely to work a lot of the time, although it's easy to imagine where it could go wrong sometimes.

    Rosenberg argues that this tendency to imagine other people's reasons for doing things (their implication graphs) is dangerous and can lead us astray when we apply it to historical or large-scale problems. A fair point. There are indeed other tools we can use for these situations, including statistical analysis, logical reasoning, and setting up empirical tests. These are the domain of System 2 - the reasoning we use when we don't want to rely on what feels good.


I am a bit more positive than Rosenberg about our reliance on narratives and theory of mind, partly because I don't believe there is any viable alternative. Maybe when analysing major historical or economic events, we can marshal the resources to apply System 2 and measure things statistically. For a limited number of oft-repeated scenarios (e.g. doing a cashflow forecast for a business) we have invested the time to build reusable tools and data sources that give a better answer than the feelings-driven System 3.

But for novel situations where fast decisions are needed, and for the everyday interactions that make up at least 95% of life for most of us, System 3 is faster, more accurate and requires less data-gathering than System 2; and is more adaptable and powerful than System 1. Stories are our best organising metaphor for the world, and if we discard them we won't be left with reliable truth - we'll be left adrift, with nothing to guide us at all.
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What people want, cognitive goods, models of persuasion and why we avoid important information: the cognitive economics session at the AEA conference
TaraECooper
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For the first time, there will be a session on Cognitive Economics at the American Economic Association’s conference. The conference, in association with the ASSA, is taking place from Friday 4 January to Sunday 6 January and will be a hugely interesting programme over the long weekend.

The Cognitive Economics session will take place on Sunday 6 January 2019 at 8-10am in Atlanta Marriott Marquis, International 10. I hope that any readers who are attending the conference are able to come along!

Let’s give you a quick overview of the session:
  • Dan Benjamin, University of Southern California; Kristen Cooper, Gordon College; Ori Heffetz, Cornell University and Hebrew University of Jerusalem; Miles Kimball, University of Colorado Boulder will be presenting a paper on What Do People Want? On the use of large-scale surveys to estimate multidimensional indifference maps over "fundamental" goods that include mental states such as emotions, perceptions and beliefs: answering the question “What do people really want?”
     
  • David Hagmann, Carnegie Mellon University; George Loewenstein, Carnegie Mellon University will be presenting a paper on Persuasion with Motivated Beliefs. Focusing on a two-stage model of persuasion in the presence of belief-protecting strategies and testing it in an incentive-compatible task. Exploring why people remain attached to existing beliefs and what it might take to change them.
     
  • Emily Ho, Fordham University; David Hagmann, Carnegie Mellon University; George Loewenstein, Carnegie Mellon University will be presenting a paper on Measuring Information Preferences. Focusing on measuring an individual's desire to obtain or avoid information that may be unpleasant, but could improve their future decisions; specifically looking at three psychological and materially consequential domains: health, consumer finance, and personal characteristics. In other words: why do we want to avoid bad news?
     
  • Leigh Caldwell [your humble blogger], founder of Irrational Agency and Inon will be presenting Cognitive Goods, Normal Goods and the Market for Information. Focusing on the relationship between cognitive goods and the value the agent is willing to pay to maintain or change these cognitive goods, the paper explains the conditions in which this thinking occurs and the consequences of this phenomena on the economy and digital markets.
     
  • Claudia Sahm from the Federal Reserve Board will be acting as Chair.
We’ve purposefully changed the structure of the session to allow for an extended closing panel with a Q&A from the audience. On the closing panel will be George Loewenstein, Miles Kimball, and Leigh Caldwell. We hope that the novel structure of the session will allow us both to share these new research papers and the big-picture perspectives on the research agenda for cognitive economics. It should be a really great way to have so many voices throughout the session.

If you would like further info on the conference, have a look here. If you would like to read the abstracts for each paper, all the details are here.

I will make sure Leigh reports back on his return about the session and the conference in general. Exciting times to have cognitive economics being recognised as the burgeoning field it is within this flagship conference.

Tweet any thoughts to me on @TaraECooper or Leigh on @leighblue.

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“Mysterious psycho-logic”, the “Nudge Unit” and irrational humans: tune in to Leigh Caldwell and Rory Sutherland on BBC Radio 4's show Thought Cages
TaraECooper
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“Mysterious psycho-logic”, the “Nudge Unit” and irrational humans - Leigh explores cognitive and behavioural economics and science with Rory Sutherland on BBC Radio 4’s show Thought Cages today and on Friday.

Tune in at 13:45 today to hear Leigh discussing behavioural and cognitive economics on Thought Cage’s next episode: Instinct Before Logic: The Postbox at the O2. During this episode, Rory and Leigh will be exploring why reason is no longer used to persuade us to change our behaviour, showing what the “Nudge Unit” of the UK government is and explaining all about behavioural science. 
Tune in to BBC Radio 4 live at 13:45, or listen to the episode on BBC iPlayer afterwards here.
If you can’t catch today’s episode, make sure you listen out for Leigh and Rory again this Friday at 13:45. Looking in more depth at the traditional shopping experience, this episode - The Sachet in the Pot Noodle - will reveal how behavioural and cognitive science is changing the future of retail.
You can listen live on BBC Radio 4 on Friday 30 November at 13:45, or tune in to the episode afterwards on BBC iPlayer here.
Thought Cages is Rory Sutherland’s new show, where he explores a selection of fresh, intriguing and iconoclastic ideas.
In other news, if you didn't catch Leigh's profile, thoughts on cognitive economics and economic blog recommendations on Inomics, make sure you check it out here!

Tweet us your thoughts on @leighblue and @TaraECooper!

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A new team member and new plans
TaraECooper
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Hello, today it’s not Leigh posting, but me, Tara, his new colleague! I’ve recently started working for Inon and with Leigh on his cognitive economics work. I’ll be writing content on cognitive economics, spreading the word about cognitive economics to both academic and general audiences and also organising events around cognitive economics. I wanted to take this opportunity to introduce myself and also fill you in on some exciting events on cognitive economics coming up.
All cards on the table - my background is not in economics or psychology. I actually studied English Literature in my undergraduate and postgraduate degrees. However, I’ve always been passionate about spreading new ideas, working in an interdisciplinary fashion and writing. In my professional work for the past few years, I’ve been programming events for two cultural institutions. I started working with Leigh in early September and it’s been a real pleasure learning and writing about this new discipline. 
The first three cognitive economics projects I’m working on with Leigh are:

So what are we planning for each of these events?
The Cognitive Economics session at the AEA will take place in Atlanta, USA on Sunday 6 January 2019 at 8-10am. Having a session purely on cognitive economics at the conference speaks to the growing interest in this burgeoning field. The four papers being presented draw together a number of academics from across the world who are keen to further the research and knowledge of cognitive economics to other academics. The abstracts for the papers can be found here.  
Outside of the cognitive economics session, a group of academics along with Leigh will be discussing the future developments and potential aims for the cognitive economics field. These conversations should have a direct impact on our thinking for the further two projects, we are working on - two cognitive economic workshops later in 2019.
Part of creating a momentum and solidifying ideas around a new field is providing spaces to think, debate and discuss ideas with peers and colleagues. This need for deeper conversation sparked the idea of having two cognitive economic workshops in 2019 - one in the USA and one in Europe - that would bring together the various pockets of economists and psychologists interested and working in cognitive economics. We’d like to go beyond the standard conference format and make these workshops much more interactive in order to help move the field forward concretely. 
There is much to confirm in terms of exact dates, locations and partners for the events. But we are confident that the workshops will help stimulate some positive movement in the cognitive economics field that should be of interest to readers and individuals in the behavioural sciences and economics more widely. More information on each event will be posted in due course.
And one last thought - keep an eye on the blog for further posts by me that will specifically focus on explaining cognitive economics to non-experts.

@TaraECooper
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Rebuilding macroeconomics
Leigh Caldwell
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Spending today and tomorrow attending the Rebuilding Macroeconomics conference at the Treasury.

The programme looks very interesting - highlights include:


  • Ekaterina Svetlova's opening talk on "Imagining the Future", which I think will be quite relevant to System 3 and an idea I have been working on, prospective expectations: the concept that actors based their decisions not on a Nash equilibrium (rational expectations) or on a simple extrapolation of the past (adaptive expectations), but on the future they are best able to imagine.
  • Sam Johnson on "The cognitive and affective processes that give rise to emergent economic order", which sounds right up my street.
  • David Laibson on "Using model free data to predict future outcomes" - more in this case for the speaker than the topic.
  • "Markets as a function of language: microfoundations of narrative economics" by Douglas Holmes - a topic that has been studied both by Robert Shiller and by Tuckett and Nikolic, and again links to System 3 if we conceive of narratives as being built in the causal graph of System 3.

At the end of the week I am heading to New York for the INET YSI's conference on endogenous preferences, at which Sam Bowles (author of The Moral Economy and Santa Fe researcher on complexity) will be speaking. At least two of today's talks (Webb Keane on the moral economy and Sam Johnson's) should connect to that.
Behavioural macroeconomics has been punted around as a topic almost since I started this blog, but hasn't really taken off as a fully developed topic with formal models (an exception is the work of Roger Farmer, one of the organisers of today's event). This conference, part of the Rebuilding Macroeconomics project at NIESR, might be a sign that that is changing. I will report back.
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Neuroscience, psychology and economics: the evidence for System 3 (long)
Leigh Caldwell
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In my last post I outlined the concept of System 3, what it is and why it matters. In short, System 3 is the mental ability to imagine the future and evaluate how happy you will be in it – based on how pleasurable the process of imagining itself is.

A lot of different research strands have come together to result in the identification of System 3 as a distinct mental process. I summarise the key steps here:
  1. The fundamental building block of System 3 is the stimulus-response relationship. It has been known for a long time that people easily learn stimulus-response relationships when they are rewarded for the response. The classic examples come from Pavlov (who rewarded dogs with food and discovered that they would start to get excited when they saw the experimenter’s white coat – as any pet owner will recognise), and Skinner (who trained pigeons to learn that pressing a lever was associated with getting fed). Although these original experiments were done on animals, there is plenty of evidence that the same principle applies to people. A typical example would be seeing the wrapper of a chocolate bar and hungrily anticipating the taste of the chocolate inside. (Stimulus-response is also the foundation of System 1 – but System 3 grows out of the same roots).
  2. The next step is the idea of successor representations. Neuroscientists (e.g. Stachenfeld et al 2017) have shown that we store in our brains the whole sequence of steps required to get to a goal. Each of these steps can be considered in turn to be an individual stimulus-response relationship. In other words, a chain of stimulus-response relations can be linked together, where the response of one step becomes the stimulus for the next.
  3. Schultz, Dayan and Montague (1997) showed that the motivational response can migrate along this chain as it becomes more familiar. Imagine the chain A->B->C represents a stimulus A that predicts response B, and B in turn predicts response C. C is the actual ‘reward’. For instance, A might be the logo of a chocolate manufacturer; B the packaging of a chocolate bar; and C the actual chocolate. As you see more chocolate packaging and open it up to discover chocolate, the packaging itself will start to motivate you before you even get to the chocolate. Then in turn, the logo might become motivating.
  4. The way that motivation changes with reward is governed by the Rescorla-Wagner model (Rescorla and Wagner 1972): if the reward experienced from an event is more (or less) than was expected, the decision maker’s brain learns to strengthen (weaken) the causal connection and is motivated to repeat (or avoid) the action.
  5. More recent work from Dayan discusses the idea of truncation: that we mentally plan the steps in a process, but we don’t plan all the way to the end. Instead we stop at some point, and base our decision on how good things look at this point. For example, a chess player might look three or four moves ahead and make a judgement about how good the position looks at that point, rather than trying to work through all the possibilities to the end of the game, which would be impossible.
  6. This in turn relates to work by on causal representations. A causal representation can be thought of as a complex network made up of individual stimulus-response ‘edges’. Sloman and Lagnado (2015) discuss how causal representations can support mental simulation and the development of narratives about the world.
  7. A separate set of discoveries was developing in parallel within the psychology literature. The idea of prospection was described in 2009 by Gilbert and Wilson in a Science article. They had observed that people think about the future, and get pleasure from doing so. This can have implications for psychological health, and more generally appears to be a common human activity.
  8. Pezzulo and Rigoli in 2011 published in Frontiers of Neuroscience “The value of foresight: how prospection affects decision making”. They worked out a model to explain how decision makers can imagine their future motivations and use these to work out what actions they will want to take in the future – and to act accordingly in the present.
  9. This work in turn builds on two core ideas. The first is the idea of model-based decision making (as distinct from model-free decisions). Model-free learning (like those early Pavlovian experiments) starts from an external stimulus and learns the corresponding action or behaviour. See a lever – press the lever. There is no meaningful representation of what the lever might mean, or why pressing it is a good thing. Model-based learning introduces an intermediate step. You see the stimulus, and in your mind you consider what this might mean, and update your mental model of the world. Model-based learning and decisions turn out to be much more powerful, especially in more complex situations, and it is likely that people use model-based representations because it would be impossible to learn enough combinations of stimulus and behaviour to reflect all of our knowledge in a model-free way.
  10. The other line of research they draw on is the idea of utility from anticipation and dread. Anticipation is when we enjoy thinking about positive events in the future; dread is when we find it painful to think about negative future events. George Loewenstein has studied this extensively (Loewenstein 1987) and determined that people do enjoy the process of anticipation, and are sometimes willing to put off a pleasurable activity in order to extend the pleasure of anticipating it.
  11. Thomas Schelling, in The Mind As a Consuming Organ (1983) had asked why we shed a tear when watching Lassie. Do we think Lassie is real, or that the things that happen to her in the show are genuine? Of course not. But we still enjoy the program: our imagination provides us with reward for ‘pretending to believe’ in this fictional world. This is likely to be connected with the psychological capacity for empathy (Ainslie and Monterosso 2002).
  12. Neuroscience work in the mid-2000s (Padoa-Schippoa and Assad, 2006; Kable and Glimcher, 2007) discovered that the brain represents reward values when we make goal-directed decisions. Rather than being rewarded for taking certain actions, we (or, at least, monkeys) are rewarded for consuming specific goods. The representation of these goods in the mind provides evidence for the idea of model-based reasoning.
  13. Recent computational learning research (Hamrick 2018, Reichert 2018) shows that mental simulation is a powerful way to solve problems, and software algorithms which use this method show similarities to human decision making. This does not directly prove that human minds decide things in the same way, but it does offer support for the plausibility of this idea.


The key step from here is to realise that model-based learning based on an underlying network of stimulus-response relations, the successor representation, causal reasoning, dopamine migration, truncation, anticipation, prospection and empathy can all be seen as different views on a common system or process: System 3.

In the System 3 process, humans maintain a mental representation of the world, structured as a causal graph: an set of beliefs about the cause-and-effect relations between events and objects in the world. They use this causal graph to make decisions. When presented with an new option, they explore mentally the likely consequences of that option: what will happen if I do it, then what will happen next as a result, and so on. (The successor representation is a specific chain of steps within this graph).

Anticipating each of these successive outcomes provides pleasure (or pain, if the outcome is negative). As a result, the decision maker experiences pleasure if the option is a good one, and this encourages them to carry out the action (and correspondingly, not to carry it out if the mental exploration is painful). The amount of reward gained from anticipation is related to the amount of reward gained from the real experience. The experience of present reward in return for future activities is what resolves the ‘prospection paradox’: how can our brains force us to forego present reward in favour of the possibility of future reward?

Observe that the decision maker can get pleasure simply from thinking about possible actions – they do not need to actually do anything! This is the key step that motivates people to prospect – the anticipation of an event, even if it will never happen, provides reward.

The Rescorla-Wagner formula pops up again now. Let’s say I think about an outcome and I am rewarded for thinking about it. My brain is rewarding me because it “wants” me to take the action I’m thinking about, because that action in turn is likely to lead to another reward (that chocolate bar). Moreover, the act of thinking about the chocolate is, most likely, statistically linked to getting and eating the chocolate – so the brain is quite right to have learned this association. But if I keep thinking about it and never actually eat the chocolate, the anticipated reward will be less than expected, and my motivation to keep imagining it will diminish. In the long run, one might expect the motivation to think about chocolate to rapidly disappear altogether: but in practice, truncation stops this from happening. The brain goes off to do other things before the reward has been fully extinguished.

So the brain is motivated to keep imagining, and ruminating over, rewarding activities. Motivation can be seen as both the fuel, and the prize, for this process; over time the fuel is metaphorically “used up” and motivation diminishes. It is likely that the motivation and reward for imagined events will move towards a stable equilibrium state. As the brain wanders around this network of imagined outcomes, it is indirectly testing out the reward levels of each event and its successors. As it simulates a chain of events and realises that the consequent reward is less, or more, than expected, it adjusts the reward assignments to reflect its more accurate prediction of how positive those events would be.

When the brain learns new rewards, it fits them into this network; and when it encounters new situations it tries to map the existing network onto the new landscape. This also happens when we watch a TV show (containing its own fictional world, of which we develop a new mental representation), or imagine the life of another person.

In all of these cases, we are rewarded for imagining what might happen – in the future, in a fictional world, or to another person – even though we gain no direct, immediate reward for any of these events. System 3 is what links the future to the present; fiction to the real world; and other people’s lives to our own.

System 3 provides the mechanism by which we come to care about, and be rewarded or punished for, purely symbolic outcomes. Typical examples are the success or failure of a work project (which we may care intensely about even if it is unlikely to affect our job security or income), a political attachment to symbols such as a national flag or a signature policy, the experience of turning 30, 40, 50 or 60 (or even 25, galling though this idea may be to many readers), the results of sporting events and many other non-material experiences. It is the impact of these experiences on our causal networks, or the mental simulations that they trigger, that provide reward or pain.

Is System 3 definitely distinct from System 1 and 2? This is a matter for judgement rather than evidence, but I would argue that:

  • System 1 is primarily about fast, nonconscious processes – while System 3, though automatic, is slower and can be quite conscious
  • System 2 processes are about accurately recreating, then symbolically and logically manipulating the material laws of the real world. For example, System 2 can tell you that if you save $1000 today, you will have $1030 this time next year. It can’t tell you how you will feel about that, or which is better. System 3, however, lets you try out the feeling of spending $1000, the smug satisfaction of not spending it, and the pleasure you may get next year from that extra $30.
  • System 3 involves a specific and distinctive mental process that is dissimilar to the instant, model-free leaps of System 1 and the emotionless, rule-based, non-causal reasoning of System 2.

I believe System 3 offers a good description of a class of decisions that are not well-explained by existing theory, and a strong foundation for understanding the economic valuation of mental states (at the heart of the emerging field of cognitive economics).


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Introducing System 3: How we use our imagination to make choices
Leigh Caldwell
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In recent years we’ve become used to thinking about decisions as “system 1” or “system 2”. System 1 choices are automatic decisions, made without thinking, based on an immediate emotional or sensory reaction. System 2 is used to stop and rationally calculate the consequences of our choices, and determine the best cost-benefit tradeoff.

But these two processes don’t capture every decision. Indeed they might only encompass a minority of our daily choices.

Recent work in neuroscience and psychology has discovered another way of making choices: with the imagination. Customers imagine their possible futures: the outcomes they would experience after a choice, and how those outcomes will make them feel. The future that makes them feel happiest will be the one they choose. These choices use different parts of the brain than System 1 and 2. They are called System 3 choices.

Think about how you might buy a car. System 1 would suggest that you see a colour, or shape, or brand of car, immediately fall in love with it and buy without thinking. System 2 implies that you calculate the price, financing options, fuel efficiency, resale value – and pick the model that makes the most financial sense.

A System 3 decision would look like this: imagine yourself driving that car. Feel, in your mind, the sensations of the seats and how it drives. Imagine how your partner or your friends would view you in it. Consider, too, the impact on your bank account and what else you would be missing out on to pay for it. How you’d feel about the environmental impact and the safety this model offers your family. How do you feel? Is it good? Maybe you also have another model in mind. Try the same process on that. Does it feel better? The car you feel best in – within this mental simulation – is probably the one you’ll choose.

This System 3 process applies to our big choices in life, but also to smaller ones. At the shelf, considering a new breakfast cereal or a new skincare product, you’ll imagine how it tastes or feels before buying it. You might test out the moisturiser from a sample jar, but you still need to project yourself into the future – will it feel the same when you’re applying it before bed, or after you wake up? Your System 3 imagination combines past and present experiences with possible futures, and works out which it enjoys most.

System 1 still has a place: once you are familiar with a product, you might buy it automatically. And we still use system 2 for lots of financial and practical calculations. Indeed, System 3 incorporates elements of systems 1 and 2 in how it works.

But if you’re:
  • Launching something new
  • Trying out a new communications or pack approach
  • Building a brand
  • Or selling something with consequences beyond just the few moments after purchase
Then your customers are probably using System 3 to make their decisions, and you need to use System 3 tools to predict the success of what you’re testing.


Some existing research tools can be used to measure System 3, and new ones are emerging too. Implicit Prospection Tools, newly designed qualitative projection techniques, and Adaptive Concept Tests are among them.

You can find out more about System 3 in my talk at IIeX Behaviour London on 10th May, or by searching for “prospection psychology”. It’s likely to be a hot topic in the coming years (use your System 3 to imagine that possible future!) – and you can get ahead of the curve if you learn about it today.
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Book review: The Choice Factory by Richard Shotton
Leigh Caldwell
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There are few truly universal books on behavioural science: like most of the others, this one has a particular reader in mind. Richard's reader works in advertising, and it must be a rare advertising executive who still hasn't heard of behavioural economics. Richard therefore heads straight into the meat of the book with little beating around the rational-agent bush. A couple of connected anecdotes start us off and we quickly get to the first of 25 chapters, each on a single bias, that make up the body of the text.

The book is very readable, and even if you already know what the fundamental attribution error, the pratfall effect and Veblen goods are, you'll probably still enjoy the stories and quotes that illustrate them. I hadn't heard of some of the experiments and anecdotes that Rich discusses - and he and his colleagues have carried out many of their own original tests - so even as a professional in the field there is much here that's worthwhile.

Structuring a book around a list of biases has the advantage of user friendliness. Each chunk is self-contained and easy to get your head around; you can dip in and read a chapter or two without needing to remember a broader framework. The natural counterpart of this is that approach can feel a little shallow. If you're already familiar with the discipline you may feel there's not much to learn from another definition of the availability bias. And inevitably several of the "biases" are not really biases: the replication crisis and "habit" are not biases, though these chapters are as useful as any of the others. Another minor drawback of this approach: because the chapters are designed to be read individually, some of the same quotes show up more than once - ever so slightly jarring if you're reading it all the way through.


The most useful contribution of the book is the original - and very good - set of practical tips at the end of each chapter. If you do work in advertising or marketing there will be a lot to get your teeth into. The first chapter alone gave me three or four ideas that I could see myself applying in the near future. Richard has a good understanding of the culture of advertising, and the book may well help people in ad agencies - or the advertising function of large companies - persuade their colleagues of the efficacy of behavioural principles.

Those in other fields may find less the book less directly practical, but there will probably be something to stimulate you in most chapters. And you can always get good stuff by following Richard on Twitter.

p.s. Full disclosure: Richard interviewed me when writing the book and you'll see some of what we discussed reflected in the pricing chapters.


Richard in conversation with Rory Sutherland at the launch of the book
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A program for cognitive economics
Leigh Caldwell
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I’m visiting the American Economics Association conference in Philadelphia this weekend and looking forward to catching up with the latest in theoretical and empirical research. Behavioural economics has received another endorsement this year with Richard Thaler’s receipt of the Nobel Prize. The behavioural field still has only a small minority of the conference’s papers, but many more than a few years ago. It finally feels like an accepted part of the broader field.
Echoes of a new discipline have started to emerge. Miles Kimball published a detailed NBER working paper in 2015 that defined cognitive economics as “the economics of what is in people’s minds”. Before that, a book and conference in 2004 discussed the topic, and a few others (including Marco Novarese and myself, here and here) have discussed it in the meantime. It seems that the term has been invented more than once in parallel - the term is after all a natural counterpart to "behavioural" economics.
The field is related to behavioural economics but takes as its focus internal psychological processes and states, rather than external behaviour. It has links with happiness research, neuroeconomics and decision making theory.
In his paper Miles lays out some suggestions for what cognitive economics should include:
  • The economics of what is in people’s minds
  • The welfare or happiness they get from their state of mind
  • The use of survey data to find out what their state of mind is


I agree with the first two, and am happy with survey data as a methodology (indeed I use it myself all the time); but I wouldn’t go as far as Miles in implying that a reliance on survey data is part of the definition of the field. It’s enough to say that it’s likely to be a tool cognitive economists commonly use. I believe that theoretical approaches, reasoning from choices and market outcomes (once we have the theoretical scaffolding to support that), experimental economics, and neuroeconomic data are just as likely to be fundamental to the discipline. (I don’t think Miles and I would have a major disagreement here.)
I am fully on board with Miles’s observations that heterogeneity, welfare and happiness, and finite cognition are all important areas that cognitive economics can illuminate. To these I would add:
  • Fictional or symbolic utility – the pleasure we gain from our beliefs (independently of their accuracy or correlation to reality)
  • The structure of preferences – an understanding of cognition allows preferences to be endogenous to an economic model, rather than exogenously given
  • The real economic effects of marketing – aside from simply providing information, marketing persuades people – that is, it changes their beliefs and preferences

And depending on the theories we develop, the field could provide insight into:
  • Intertemporal tradeoffs, why they exist and how they are made
  • Social transmission of beliefs and preferences
  • Empathy for others and vicarious utility


I may be getting ahead of myself here. Before we can have such ambitions, I would suggest that some of the next steps in developing a successful theory of cognitive economics should be:
  1. Continuing to build alternative theories of individual decision making (DM), to supplement or replace utility maximisation. Miles talks about “new theoretical tools for dealing with finite cognition” and I agree this needs to be a core part of the program. Examples include Gabaix’s sparse maximisation models, and my own work on prospection. Much of the work in judgement and decision making would fit into this area, but a lot of it is oriented towards explaining individual decisions and not amenable to theoretical use in economic models. Of course we should avoid the trap of making assumptions about DM for the sake of modelling convenience rather than accuracy. However, it is definitely possible to make simplifying choices that retain more realism than rational agent theory, and provide more modelling power than empirical psychology.
  2. Using these DM models to explore important phenomena that are observable and have economic consequences. The models will only be used if they are useful; I believe we can show their value by explaining things like advertising, the evolution of preferences, and the significance of media consumption in our lives.
  3. Working out how markets operate under the constraints of finite cognition. The classical microeconomic models are beautiful but rely on utility maximisation to work. Many writers have observed ways in which markets depart from these ideals – which often have political implications, from Hayek’s “Road to Serfdom” on the right to Shiller and Akerlof’s “Phishing for Phools” on the left.
  4. After this, we might eventually look for macroeconomic – or macrosocial – principles of how whole economies and societies work on cognitive economic foundations. It is likely to be some time before the theoretical foundations are ready to support that kind of work.

   Steps 1, 2 and 3 are likely to happen in parallel. When we attempt to apply our DM models to illuminate economic phenomena and markets, we will receive essential feedback on how good those models are.
I plan to organise a small conference or working group to discuss some of these topics and work out a research agenda. 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The amoeba and the squirrel
Leigh Caldwell
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[An essay written for the Internet Review, a one-off maybe-to-become-annual publication documenting (and celebrating?) Internet trends]

Every human has two minds: one like an amoeba and one like a squirrel. The amoeba mind is reactive, emotional, intuitive. It decides immediately, without planning or consideration. It is Freud’s “id”, or the System One of behavioral economics: the amoeba is your unconscious. Your squirrel mind plans, trades off immediate pleasures for future gain, is capable of abstract reasoning and cooperation – the superego.

Being an amoeba is often more fun – maybe even more authentic – but the squirrel makes things happen in the long run.

Society also has amoeba and squirrel modes. The amoeba is the local interaction: follow your senses and do what’s in your direct interest, consequences be damned. Squirrel mode requires bigger institutions, and trust: in other people’s knowledge, a shared logical picture of the world, forgoing today’s profit for society’s long-term benefit.

Until now, newspapers, TV and political parties have been democracy’s squirrels, fact-checking and interpreting for the rest of us. In 2016, social media – the ultimate amoeba forum – became pervasive enough to challenge squirrel norms. Society’s truth is no longer mediated by squirrels. Amoebas transmit and amplify emotional messages; the amoeba mind gains an unfiltered political life of its own. Amoebas have no plan, but together they have power.

Facebook is America’s collective unconscious; a swarm of amoeba minds, riven by the conflicts and irrationalities of instinctive urges. Until we teach ourselves to think about consequences again, humanity will have the self-control of a 4-year-old. Twitter, a bridge from amoeba to squirrel, might show a way. But as 2016 ends the squirrel still seems to be in hibernation.
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The gender pay gap on Euristica: an imaginary island
Leigh Caldwell
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I recently gave a talk at TEDxCoventGardenWomen about an economic agent-based modelling system I have built (readers of Thomas Schelling may see some influence). In the talk I use this system to analyse ideas around privilege, prejudice and systemic inequality - and to test some policies that might help to solve the persistent gender and racial pay gaps that we still see in most societies.

The video is below - your thoughts would be very welcome.

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Discussion 3 of 3: Lassie died one night
Leigh Caldwell
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The much-delayed final episode in a short series of posts - part 1 and part 2 here.

Lassie died one night.

As Thomas Schelling* pointed out in a thought-provoking 1982 essay, millions of people watched it happen on television one Sunday evening, and cried. Yet they all knew Lassie was not real – and that the dog who played her was probably in perfect health. Why did they experience the same emotions, the same sense of loss that they would expect to feel if their own dog had died, or even their own grandfather? Why should fictional outcomes and situations provide us with (positive or negative) utility? (And if they do, why can we not simply conjure up unlimited happiness by indulging in films or books that we enjoy and hiding from the world?)

The two hypotheses laid out in the previous posts can provide an explanation not only for this, but for a number of other psychological phenomena:


  • H1: That potential decision outcomes are automatically evaluated by an associative network representing events that might possibly happen
  • H2: That the process of imagining future reward generates real reward in the brain


Start with H2. If imagining future reward – or, by extension, future pain – generates real reward or pain, then it is not a big leap to think that imagining a sad outcome (Lassie’s death) would cause real sadness. Our brains have evolved to allow us to imagine possible outcomes for ourselves, because we need to choose between them. If I was actually involved in the situation and had to choose between a course of action that leads to Lassie’s death and another which does not, it would be important that my mind be able to envisage the consequences of each action, imagine what the results might be and see which one I enjoy more.

H1 claims that this reward-imagining mechanism operates automatically, without our conscious intervention. If this is the case, perhaps that mechanism cannot distinguish whether the outcomes it is evaluating are fictional or not. The mechanism must be disconnected from sensory input – after all, its job is to imagine outcomes that are not presently being sensed. In a way, all of its inputs are fictional: an imagined future for the decision maker, or an imagined present for Lassie.

Let me then propose a third hypothesis: that watching a film, or reading a book, can expand the network of possible events in your head, by adding its own (fictional) concepts into that network. Even though you know it is not real, you construct a mental model that represents the people, objects, events and outcomes in the fictional world. You do this because it is usually pleasurable to imagine and evaluate these possible outcomes; this is the source of the enjoyment we gain from engaging in an imaginary world.

So in summary, the brain contains a model of possible events, which is continuously and unconsciously evaluated to see which outcomes are better. The process of evaluating the events generates reward, in proportion to how rewarding the events would be if they really happened. Reading or watching fiction can add events to this model – as our senses interpret the information, they dump it into our mental model of the world. And therefore, the brain will engage in evaluating events, fictional or otherwise, and be rewarded for doing so.

The sadness you felt when Lassie died – that’s an inevitable side-effect. You watched Lassie for the pleasure, and it tricked you into feeling the pain too.

The consequences of this model go beyond just why we watch TV. Your empathy for another person can be motivated by exactly the same mechanism: you imagine their life outcomes and evaluate them for pain and pleasure, just as you imagine your own. You experience that pain or pleasure while imagining it.

It can explain why a person experiences pleasure or pain from completely symbolic events – the success of a football team, or their government’s implementation of a policy that will never directly affect them. And it suggests why people might adopt specific beliefs in the absence of objective evidence for them.

Although these ideas may not individually seem revolutionary, together they offer a different way to think about the psychology of decision making.

  • Instead of assuming that utility is discounted exponentially in time, consider that it may be discounted by its causal distance from the current situation.
  • Instead of assuming that outcomes can be evaluated in isolation, think of them as only the endpoint of a chain of causes and effects.
  • Instead of assuming that material consumption is evaluated for the utility it provides, think about the desire to control one’s own mental state as the primary object of human behaviour.

This model could allow a much simpler theory of choice than we have been used to. It no longer requires all the axioms that underlie subjective utility theory. Stable, exogenous preferences over goods; the existence of time discounting; transitivity; all become parameters of the model instead of requirements. Some phenomena that are treated as biases in the standard model may emerge as natural consequences of this decision process: loss aversion, confirmation bias, non-independence of irrelevant options.

This is still a somewhat speculative proposal, but there is decent evidence from neuroscience and philosophy that is consistent with it. My current research involves developing the mathematics behind this model, and some experiments by which it can be tested.


* Schelling himself died just a couple of months ago, which makes me sad even though I never knew him.
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How does it feel to be part of Europe?
Leigh Caldwell
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I had this piece drafted before the murder of Jo Cox last week. But I don’t think it changes anything I was going to say. It simply makes it more urgent to say it.
May I introduce you to my two lovely young nieces? Natasha is four months old and Rosalind four years. They live in rural Devon, and they’re just starting to discover the world and decide how to feel about it. I want to think a little about what it might feel like to be in their world.
The campaign for Britain to remain in the EU has been full of facts and utilitarian arguments. Economic projections, dispelling of myths about regulations, estimates of the economic and tax contributions made by European workers in Britain. All the kinds of things that may convince you if your inclination is to weigh up the numbers and evaluate the facts.
But there are plenty of people who don’t want to make a decision based on numbers, and I understand that. Numbers can be manipulated. We don’t all have the time or desire to read and check and compare statistics. Some decisions are always going to be made on intuition. And the deepest, most powerful intuition is our emotions.
This is the ground the Leave campaign chose, and they’ve commanded it skilfully. They identified and tapped into angers and fears held by millions of people. For some, they have amplified that anger; for others, they have offered permission to vocalise something that was smouldering under the surface. There is no point trying to combat an emotion with facts; and perhaps more importantly, we have no right to tell anyone their emotions are not valid. That feeling belongs to them and they are entitled to it.
The emotions involved are of course more subtle than plain anger. It’s a web of linked feelings and experiences, arising from a set of messages that we’ve all heard and that we all interpret in different ways. So I wonder how Rosalind and Natasha will grow up interpreting those messages, what feelings they will have about Europe and about the relationship they want to have with other countries.
Maybe Rosalind will be among the many people who now experience more competition for jobs than they would have expected twenty years ago. Maybe she will feel the stress of applying for a series of jobs with no certainty of winning any of them. Economists might try to reassure her that if someone from Italy gets a job here, the money they earn and spend will (on average) create another new job, and she can apply for that one instead. But that’s too abstract to feel like a fair exchange, and many people simply don’t believe it.
The social environment they’ll live in has changed too. They’ll hear more languages around them than their grandparents and parents did in the 1950s or the 1980s. More kinds of food are sold in their local shops and more religions are practised in their neighbourhood. The world in general will be more complex and it may be tough to work out how to navigate it confidently. There’s a real psychological reason behind this. If I have less in common with my neighbour – in culture, behaviours, values, profession, clothing – or for that matter sexuality, politics, gender or ethnicity – I will know less about how they’ll react to my choices and actions. And we are social animals. Understanding our neighbours is a necessary part of living in a modern society. Living alongside difference makes it more taxing to work out the strategies of life.
Natasha, like us all, will make instinctive judgements about who she wants to govern her. Legitimacy in government does not arise only from democratic constitutional arrangements. Natasha will want to see something of herself in her leaders. Maybe even something a little more than herself – someone she is willing to put her trust in, to make better and more informed decisions than she would. Leaders have to have enough in common with those they govern so that we feel they can understand us and represent our interests. I can understand that it is easier to see that commonality in a politician whose face we see on TV every week, who probably speaks the same language as we do, than in someone more distant – regardless of their policies and beliefs.
On top of this, imagine the two of them are faced with a profound, existential fear about what is happening to the world. Conflicts in distant countries seem to be on their doorstep suddenly. Bombs burn down cities and their millions of terrified inhabitants flee – northwards and westwards.
Would it be any wonder if Rosalind and Natasha reacted with fear, suspicion, anger, to want to close and bar the doors, to give their vote to the people they know best, who look and sound most like them?
Maybe not. And yet.
There is also another way to respond, which arises just as naturally from who we are as human beings. The same facts – and even some of the same feelings – might form a different constellation.
In many of life’s spaces, difference is not a threat but a source of happiness. Think of the diversity we enjoy in our friends, in our food, in our football players, in the beer or wine we drink, the styles of clothes we wear, the cars we drive. The phones we use, the music we listen to, the people we desire, the films we go to see. Britain can be proud of its contribution in most of these fields. At the same time, we are all better off for having access to what everyone else makes too. A kebab with your Carling, or a claret with your Cheddar – what could be better? And if Rosalind in a few years feels like changing her environment a bit more – if only for a fortnight – she can hop on a flight to Prague or Marbella or Crete to be part of someone else’s town, and see what their life is like.
It’s a short step from there to a chat with the Polish or Spanish family who live on the farm next door and work in the village shop. They chose to come here from their own home. They’re probably admirers of our culture, who want to adapt to it, not change it. We like to live in societies where not everyone is the same. Not everyone I know does the same job, supports the same team or does the same thing on a Friday night, and I wouldn’t want them to. We’d have nothing to talk about. The distance between someone I don’t understand at all, and someone who is simply different, is only a conversation.
Once I’ve spoken to my Muslim or Orthodox Jewish neighbour, or the Hungarian barista in the café, I have an idea of how they live their life and how it differs from me. It becomes much easier to live alongside them without awkwardness or mistrust. Suddenly that different culture is a source of new ideas and things to talk about, instead of a threat.
For some people, the perception that it’s harder to find a job will be correct. If Natasha can’t find work in what she is trained for, it will be tough. But at least she will have the chance to explore the rest of Europe and look there – which will be easier if she has grown up alongside French and Polish and German neighbours than not.
Those new workers are also, however, filling one of the biggest holes in the lives of our communities – they are taking care of our parents and grandparents. Availability of care workers and medical staff genuinely makes the lives of patients much better, whichever country they come from. If the modern economy doesn’t make it easy for my sister, brother and I to live with and care for our own parents, I hope there will be a dedicated and friendly person – from whatever country – who can step in and help. And while my parents fortunately don’t need care yet, they both live in rural areas where the availability of builders, electricians and pub workers from both Britain and Europe is essential to their quality of life. I know they appreciate the services those people provide, whatever their accent.
As well as listening to our neighbours I hope we can also listen to the people who help make our laws; and to recognise the dilemmas they face in balancing different interests in society. The balancing act carried out in Westminster is just as hard as the one faced in Strasbourg. Our politicians, mostly, have good intentions and try to strike the balance they think is right. We’ve heard tributes to the spirit and values of one in particular in the last week, but most other MPs, and most MEPs, and most commissioners or cabinet ministers, are like her too. We should keep holding them to account democratically, to make sure the choices they make are in line with the values we see in ourselves and want our governments to reflect. While doing this, we can sympathise with them too.
If some decisions made at European level are a compromise between our needs and those of other countries, we can speak up and make sure our opinions are heard. And we won’t mind making those compromises occasionally, because we care about what others need as well as about ourselves.
One of the toughest decisions those representatives face is what to do when two million people are suddenly homeless and in mortal danger in Syria, across a narrow sea from the (relatively) well-off, safe communities we live in. Those people have a way of life even more different from ours than the Slovenian or Greek who works along the road from you. It may be harder to empathise with them; and yet the power of our humanity is that we do it anyway. We, or at least our parents, still remember wars on our own land; refugees we sheltered; the bonds of common purpose that gave us the strength to overcome hate seventy years ago and rebuild the democracy that we are so proud of in this continent.
Our life in Europe can be based not on fear, but joy. My nieces, and my cousins a few years older, are young enough to simply have fun with Europe; to remix its different cultures, learn a few words, drink its wines and eat its foods; to bring over there the things that other Europeans love about Britain (Adele, Shakespeare, whisky, the BBC, the Rolling Stones) and swap them for the things we like about them (Daft Punk, Sophia Loren, mozzarella and BMWs). To get a Dutch boyfriend or a Croatian girlfriend, spend a year in a Spanish university, or backpack around the Balkans. I am excited by the idea that the territory I live in has every kind of landscape from desert to snow, lush valleys and hot beaches, cold seas and warm ones; that I can eat deep fried cod, fresh oranges and Dairy Milk chocolate all from the same brilliant, mixed-up continent.
I know we can still get some of this by trading with Europe from the outside, but if we leave, the basic assumptions that we live with, how we perceive our identity, will change. This choice is not really about the practicalities but about the principles our world lives by. And there are still some things we can’t have, and can’t help with, from the outside.
Our relationship with those slightly different people who live a few hundred miles away need not be one of anger. Instead, let it be one of love. We should be proud of an amazing, under-recognised gift we have been able to give to a dozen ex-communist countries: to accept them into a community that has enabled both them and us to become richer. Poland is the fastest-growing major economy in Europe and millions of people are now several times better off than they were 20 years ago. Richer in money but also in culture and friendship. It may be their turn in the next few years to pass that gift on to a few countries a little further east, or south. We may choose to help then too.
And by the time Rosalind and Natasha are my age in a few decades, that gift of love and investment will have been repaid many times. The cultural riches, the human contact, the things they will buy from us with their newly grown economies – and most importantly of all, the wars we will never know about because they didn’t happen. The peace that Britain helped bring about, that Western Europe has built with us, and that has gradually crept eastwards, will roll two thousand miles further.
If we let fear and anger give way to joy and love tomorrow, this is the future that is available. I hope this is how Natasha and Rosalind will grow up thinking about Britain and Europe, about us and the Europeans who visit us. They and their own children could grow up in a country cut off and a world that distrusts itself, or they could play a full part in a world where we and our neighbours have fun together and care about each other. If you think I’ve described a global society that you and your children might like to live in, I hope you’ll feel comfortable voting on Thursday to keep our European friends part of the family.

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