I’d recommend Nate Silver’s  The Signal and the Noise: Why So Many Predictions Fail— but Some Don’t to anyone looking for a layman’s tour of applied statistics. It is not a “how to” book, although there are plenty of principles (and suggestions to be humble) worth following. It’s also not a book that gets too deep into any subject area, so for those areas I was familiar with, there were no surprises. But where I wasn’t, I generally enjoyed it.

The section of the book I enjoyed most was the section on weather forecasting. As a start, weather forecasting is getting a lot better. In the early 1970s, a temperature forecast by the US National Weather Service three days in advance tended to miss the actual temperature by around 6 degrees F. Today, they miss by around 3.5 degrees F. For hurricane forecasts 25 years ago, the prediction of where a hurricane would make landfall three days in advance had an average miss of 350 miles. Today it is marginally over 100 miles. For Hurricane Katrina, the prediction of landfall at New Orleans was made five days in advance.

It’s cool to see that there is progress in forecasting in complex systems such as the weather. But the current limits to predictive ability are also interesting. Silver presents a chart comparing the accuracy of temperature forecasts from three sources: climate averages, persistence (the weather tomorrow will be the same as today) and commercial forecasts. Predicting the climate average for any number of days in advance of today results in an error of around 7 degrees F. Persistence outperforms the climate average for the next day, but for more than two days ahead, you are better off assuming the climate average. Finally, commercial forecasts start out as much superior to assuming the climate average, with an average error of around 3 degrees F for the next day. But by day 8, the commercial forecasts barely beat the climate average. After day 10, the commercial forecasts are worse.

What is interesting is that the commercial forecasts rapidly deteriorate to a point where they display negative skill after 10 days. You could simply look to historical averages for a better indication. This is caused by feedbacks in the computer program, which start to build on themselves. With the programs highly sensitive to the initial conditions, the noise ends up dominating (Silver gives a useful simple synopsis of chaos theory).

The weather chapter provides an interesting contrast to the chapter on economics. Economic forecasting might be considered similar to weather – a dynamic system sensitive to initial conditions – but with a markedly different record of improvement. And not only is economic forecasting demonstrating little improvement, but the confidence with which economic forecasts are made is far in excess of what they deserve.

A couple of issues underlie this. First, economics does not have the same fundamental laws underlying it (and as I often argue in posts in this blog, those that exist are too often ignored). But also, the incentives are different. Weather forecasting is tested day after day, so people note errors. There is a small degree of bias where there is something at stake, such as some forecasters exaggerating the probability of rain when it is unlikely to occur, because people remember rain ruining an event when the forecast was for sun. And local TV weather presenters push this wet bias even further. But in general, performance is not too bad.

For economics, predictions are rarely tested and often made years in advance. There are strong incentives to herd where being wrong and the only one who is wrong is costly. There are also incentives to make outlandish claims when people will forget all the other times you are wrong (keep predicting the financial crisis, and you will be right sooner or later). It’s hardly an environment where accuracy is the best bet.

Otherwise, the chapters on disease and chess were also excellent. I wasn’t a big fan of the climate change chapter (neither was Michael Mann) and I still don’t understand why Americans like baseball.

I rate Dan Ariely’s The Upside of Irrationality: The Unexpected Benefits of Defying Logic lower than Predictably Irrational. Like Predictably Irrational, The Upside of Irrationality is based largely on Ariely’s own work (a good thing). But where Ariely had 15 years of experiments to call on for his first book, for this one he seems limited to a couple of years of newer experiments and the experiments in the reject pile from the first. We then get a lot of filler where Ariely riffs on the theme of the experiment rather than reporting experimental results. It causes this book to feel lightweight in comparison to the first.

That said, I enjoyed the first couple of chapters about work incentives. Ariely and his colleagues ran experiments where they offered incentives to experimental subjects for the performance of mentally taxing tasks, such as remembering a sequence of numbers or hitting a target. But rather than incentives improving performance, high incentives (in the order of several months pay) caused the participants to choke and perform worse than those who were moderately incentivized. This contrasts with experiments that required purely mechanical activities for bonuses, with larger bonuses generally increasing performance.

Ariely related a story about telling a group of bankers about these experimental results, with the bankers suggesting this incentive problem did not apply to them. In some senses, I agree with the bankers, but likely for different reasons. The crumbling in performance witnessed by Ariely and his colleagues was for short-term mentally taxing tasks. In contrast, most bankers, consultants, lawyers and the like are receiving bonuses for a year of effort, making the bonus less salient at any time. But more importantly, the bonuses are heavily tied to the mechanical part of the job – putting in or billing a massive number of hours. I expect there are not many split second decisions that are required to be made with the bonus in mind, and Ariely is overestimating the short-term creativity required.

I find these experimental outcomes somewhat perplexing from an evolutionary perspective. What is the benefit to choking when the stakes are high? One explanation might be that in the environment of evolutionary adaptedness, high-stakes games often ended in death, with choking a signal for the person to get out of there. Another might be that most high-stakes events in that environment simply needed a fight or flight response, not the maintenance of mental coordination.

It was when Ariely got into areas such as dating that the experiments seemed thinner and Ariely was forced to fill more space with his personal views on the subject. I don’t mind a bit of speculation, but Ariely spent a lot of time extending his discussion beyond the experimental context than in Predictably Irrational.

For example, Ariely reported the results of an experiment where, before a speed dating event, participants engaged in a virtual online date where they explored a virtual space together. Those who had earlier participated in the virtual date with their later speed dating partner liked them more. Ariely suggests this indicates a flaw in speed dating setups. But what is the objective of speed dating? Does the increased probability of liking someone due to an earlier virtual date, even though the characteristics of that person have not changed, lead to achievement of the goal of a long-term partner? Or is that familiarity leading them to ignore more suitable people in the room during the speed dating?

Ariely also looked at online dating, and noted that huge amounts of time are expended online relative to the time spent on dates. People do not rate the experience as enjoyable. He saw this as a general indication of the failure of the dating market. I won’t claim that dating markets are perfectly efficient, but again, what is the objective of online dating? I expect it is not enjoyment. If the purpose of online dating is to create a large pool from which the dross can be weeded out, the counterfactual for comparison is going on dates from a smaller pool without that filtering mechanism. Which is the better option? I don’t know the answer to these questions, but I’m not convinced Ariely did either. But as there seemed to be a need for filler around the experimental results, we got a lot of Ariely’s thoughts on these subjects.

I absorbed The Upside of Irrationality in the right way – through an audiobook on my way to and from work. It’s an easy read/listen, has some interesting ideas (particularly early in the book) but seems light compared to Predictably Irrational. I’ll keep Ariely’s next book, The Honest Truth About Dishonesty: How We Lie to Everyone–Especially Ourselves, on my reading list, but I hope it is more dense with research results than riffs on the theme than is the case for The Upside of Irrationality.

Links this week:

1. Kids born earlier in the year do better in sport, but this doesn’t seem to apply for academic outcomes.
2. Rational crack addicts.
3. Why life at 40 doesn’t seem so great.

Otherwise, this wasn’t the most exciting week for links.

I am called an evolutionary economist often enough that I have been tempted to write a post titled “Why I am not an evolutionary economist”. In the absence of that post, Peter Turchin quizzed Ulrich Witt on what evolutionary economics is, and provides a useful description:

[T]here are two main currents in evolutionary economics, which have developed largely independently of each other. One research direction, within which Ulrich himself has been working, begins by questioning the assumption of homo economicus, a rational agent that choses those actions that yield the best balance of rewards versus costs. Real human beings behave in a very different way. …

However, it’s not enough to say that we fail to measure up to the lofty standards assumed by the rational choice theory. What would be particularly interesting is to understand in what ways our behavior deviates from ‘perfect rationality’ and why we evolved to behave in these ways. In the last couple of decades the fields of evolutionary psychology and behavioral economics have been making great strides in answering such questions.

The second current in evolutionary economics is sometimes called the ‘Universal Darwinism’ … Darwin developed his theory to explain biological evolution. But his basic insight has a lot of value when considering the dynamics of economic agents (especially, organizations such as firms and corporations) competing in the market. In biological organisms evolutionary ‘fitness’ is maximized when they increase their chances of survival and reproduction. Firms also have fitness, which is maximized when they increase their revenues and cut costs.

I haven’t heard evolutionary economics described in this split way before, and have tended to see both “currents” as encompassing the cultural evolution of economic systems. Witt (and others) place the evolutionary focus on the evolved human tendencies that underlie the economic dynamics. The second approach (as epitomised by Nelson and Winter’s seminal work) places the evolutionary emphasis on the dynamics themselves. Importantly, Witt’s approach displays some skepticism toward Darwinian approaches to cultural evolution, with non-Darwinian considerations such as diffusion and learning also playing roles.

Unlike Turchin, I would not describe evolutionary psychology or behavioural science (I prefer “behavioural science” to “behavioural economics”) as having made great strides in this area. Behavioural science is a body of work crying out for the theoretical backbone that evolutionary biology could provide. Yet evolutionary biology is not considered in most behavioural science work. It is why we have so many lists of heuristics and biases and so little theoretical unification (giving a bias a name is not theory). Evolutionary psychology has been more fruitful, but is not often enough turned toward economic applications or the empirical findings of behavioural science.

The work of Witt is a rare example of those evolutionary insights into human behaviour being used in an economic context (here is another), but I wonder if the limited spread of these ideas through economics is due to it being within the evolutionary economic framework. Evolutionary economics is a heterodox field on the fringes of economics, so combining the evolutionary approach to human behaviour with a second heterodox approach creates one barrier too many.

Then again, many of those working on the evolution of preferences have expressly taken more orthodox approaches (for example, Arthur Robson notes this on his homepage), but are also some way from transforming economics. In that case, the extent of their impact might be attributed to the failure of that evolutionary approach to explain many of the empirical observations developed by behavioural scientists. Evolutionary approaches have a habit of generating rational preferences.

What does give me hope is that much recent work at the frontiers of behavioural science is driven not by economists or psychologists, but by evolutionary biologists. At times it seems as though they are repeating empirical work done by others before them, but with an evolutionary framework with which to analyse it, those observations take on a whole new light.

As for the label for my work, I am tending towards “Darwinian economics”. I’m not sure if it will work, but it avoids the baggage that comes with “evolutionary economics”, “bioeconomics” and other labels with longer traditions. It is also less of a mouthful than “the integration of economics and evolutionary biology”.

Links this week:

1. David Dobbs on the social life of genes. Full of interesting ideas, although I’d love to see someone who knows more about this area critique it.
2. Tim Harford reviews Scarcity.
3. A good review of some new books on neuroscience.
4. Dave Nussbaum on why you are working too hard.
5. Ian Rickard pulls apart David Attenborough’s suggestion that humans are no longer evolving.
6. And below, the latest two videos in the Evolution: This View of Life series On The Origin Of Human Behavior And Evolution Society: Doug Kenrick and John Tooby.

In Tim Harford’s article contrasting Lin Ostrom and Garrett Hardin‘s approaches to the tragedy of the commons, he writes:

She [Ostrom] persevered and secured her PhD after studying the management of fresh water in Los Angeles. In the first half of the 20th century, the city’s water supply had been blighted by competing demands to pump fresh water for drinking and farming. By the 1940s, however, the conflicting parties had begun to resolve their differences. In both her PhD, which she completed in 1965, and subsequent research, Lin showed that such outcomes often came from private individuals or local associations, who came up with their own rules and then lobbied the state to enforce them. In the case of the Los Angeles water producers, they drew up contracts to share their resources and the city’s water supply stabilised.

It was only when Lin saw Hardin lecture that she realised that she had been studying the tragedy of the commons all along. …

In his essay, Hardin explained that there was no way to manage communal property sustainably. The only solution was to obliterate the communal aspect. Either the commons could be nationalised and managed by the state – a Leviathan for the age of environmentalism – or the commons could be privatised, divided up into little parcels and handed out to individual farmers, who would then look after their own land responsibly. …

But Lin Ostrom could see that there must be something wrong with the logic. Her research on managing water in Los Angeles, watching hundreds of different actors hammer out their messy yet functional agreements, provided a powerful counter-example to Hardin. She knew of other examples, too, in which common resources had been managed sustainably without Hardin’s black-or-white solutions.

Ostrom identified eight design principles that allow groups to manage common pool resources sustainably. Without them, we’re closer to Hardin’s conception of the tragedy. These principles are:

1. Clearly defined boundaries
2. Congruence between appropriation and provision rules and local conditions (i.e. proportional equivalence between benefits and costs)
3. Collective-choice arrangements – those affected by the rules can participate in modifying the rules.
4. Monitoring
5. Graduated sanctions
6. Conflict-resolution mechanisms
7. Minimal recognition of rights to organize
8. For larger systems: Appropriation, provision, monitoring, enforcement, conflict resolution, and governance activities are organized in multiple layers of nested enterprises.

In the special issue of the Journal of Economic Behavior & Organization, Evolution as a General Theoretical Framework for Economics and Public Policy, Ostrom, together with David Sloan Wilson and Michael Cox, proposed that these principles can be generalised beyond the common pool resource problem. First, where these principles hold, they also provide the ideal social environment for the evolution of group-level adaptations. If you have read any of my earlier posts on the special issue (links below), it comes as no surprise that multilevel selection theory forms a strong part of Wilson, Ostrom and Cox’s evolutionary argument. Second, the principles can be applied to a wider range of groups.

The most interesting application of this argument relates to the first design principle, the need for clearly defined boundaries. When group selection was first framed as a concept, it was seen as competition between groups in the way that an ordinary person might see it; say, different tribes fighting against each other. But this style of group selection ran into problems in that the group boundaries are not as clear as one might think. People move between groups. The victor in war takes the women, children and surviving males into their own group. This lack of clarity was a central plank of many criticisms of this style of group selection.

The conception of group selection labelled as multilevel selection theory averts this problem through adopting a more flexible concept of groups. Individuals take part in many groups, which each group differing by context. For example, a person may have many trading relationships, each being conceptualised as a group. They have family groups. They form coalitions. As such, there can be considered to be clearly defined boundaries for each of these different groups. (For earlier posts discussing how this multilevel selection framing works, see here and here) But when put this way, the group boundaries can appear to be more a question of framing than substance. And given the equivalence of a multilevel selection framework to inclusive fitness, it is possible to ignore the framing of group boundaries and simply consider whether an individual and their relations benefits from their action relative to the broader population.

This equivalence of framing is also apparent in the second design principle. The costs and benefits within and between groups are the core of the multilevel selection calculation, whereas an inclusive fitness framework utilises Hamilton’s rule to balance costs and benefits weighted by relatedness.

Of the other design principles, the presence of monitoring, sanctions and conflict resolution mechanisms create the costs and benefits that induce cooperative, trusting behaviour. Where these principles hold, we might expect social behaviours to be fitness enhancing. Also, when people interact it is easy to frame those social behaviours such that most of the multilevel selection dynamics are at the group level. Simply find those who socially interact and call them a group.

This is the point where I tend to question whether a multilevel selection framing is the best approach. Once the definition of a group drifts from that that is commonly used, it loses simplicity and transparency. It also hides the reason for the group interaction. When someone undertakes a trade, do they consider foremost their standing relative to their particular trading partner, or to a larger population? The evolved psychological mechanisms that lead to the formation of the “group” in which the trade occurs may also be hidden in the multilevel selection framing.

As a result, I can see the alignment between Ostrom’s design principles and the multilevel selection framework. But when the definition of a group is so flexible, I’m not convinced that this is a useful way of examining the problem.

I don’t have much to add on the second claim – that Ostrom’s design principles can be applied to a wider range of groups – so will leave that point for now.

My series of posts on the Journal of Economic Behavior & Organization special issue, Evolution as a General Theoretical Framework for Economics and Public Policy, are as follows:

1. Social Darwinism is back – a post on one of the popular press articles that accompanied the special issue, a piece by David Sloan Wilson called A good social Darwinism.
2. Four reasons why evolutionary theory might not add value to economics – a post on David Sloan Wilson and John Gowdy’s article Evolution as a general theoretical framework for economics and public policy
3. Economic cosmology – The rational egotistical individual – a post on John Gowdy and colleagues’ article Economic cosmology and the evolutionary challenge 
4. Economic cosmology – The invisible hand – a second post on Economic cosmology and the evolutionary challenge 
5. Economic cosmology – Equilibrium – a third post on Economic cosmology and the evolutionary challenge
6. Design principles for the efficacy of groups (this post) – a post on David Sloan Wilson, Elinor Ostrom and Michael E. Cox’s article Generalizing the core design principles for the efficacy of groups

From Smithsonian magazine (HT: John Hawks):

Though northern muriquis are critically endangered, the population in Strier’s study site, which is protected from further deforestation and hunting, has increased. There are now 335 individuals in four groups, a sixfold increase since Strier started her study.

That’s a development worth celebrating, but it’s not without consequences. The monkeys appear to be outgrowing the reserve and, in response to this population pressure, altering millennia of arboreal behavior. These tree-dwellers, these born aerialists, are spending more and more time on the ground. At first the behavior was surprising. Over time, though, Strier made some sense of it. “They’re on an island, with no place to go but up or down. When humans didn’t have enough food, they invented intensive agriculture. Monkeys come to the ground. It makes me think of how hominids had to eke out an existence in a hostile environment. Our ancestors would have brought to that challenge the plasticity we’re seeing here.”

Initially the muriquis descended only briefly and only for necessities, Strier says. Now they’re staying down for up to four hours—playing, resting and even mating. …

Strier wonders about the potential for other changes. What will peaceful, egalitarian primates do if crowding becomes more severe and resources run short? “I predict a cascade of effects and demographic changes,” she says. Will the monkeys become more aggressive and start to compete for food and other essentials the way chimps and baboons do? Will the clubby camaraderie between males fall apart? Will the social fabric tear, or will the muriquis find new ways to preserve it? Strier has learned that there is no fixed behavior; instead, it’s driven by circumstances and environmental conditions. Context matters.

For humans, the stagnation in income in the Malthusian world hid an underlying dynamism as people competed for scarce resources. Each innovation that increased resources allowed population density to increase. In a similar way, the muriquis are able to increase their density through a new innovation, moving to the ground.

In the human case, the innovation in the Malthusian state was ultimately the seed for the Industrial Revolution. The muriquis are some way from that, but it is possible to see it on the same spectrum of change that humans have undergone in the past.

Links this week:

1. David Sloan Wilson and Jonathan Haidt have kicked off an evolution and business blog at Forbes. It will be worth a read, and unsurprisingly the first post reflects Wilson and Haidt’s group selection leanings. It should give plenty of fodder for interesting posts. I’ve written about Haidt’s group selection views before, plus plenty of posts on Wilson’s (here and here for starters).
   
2. Also on Jonathan Haidt, he is the editor of a new business section at Evolution: This View of Life.
3. Two new books that look worth a read: Big Gods: How Religion Transformed Cooperation and Conflict and The Rational Animal. Doug Kenrick posts on the latter.
4. Tim Harford’s article on Lin Ostrom’s work is good. Ostrom gets quoted a lot for her suggestion that a single international agreement to deal with climate change would be a mistake, less so for her suggestion of polycentric action at all levels – just the sort of thing that would have the typical economist decrying as horribly inefficient.

In 1798, Thomas Malthus described a world where technological progress did not increase per person income. Any additional income was consumed by population growth. It appeared a solid explanation of the world to that point, but Malthus had the misfortune of describing the “Malthusian world” just when some parts of that world were breaking their Malthusian shackles. This left Malthus with a somewhat tarnished reputation (I consider undeservedly), with the Malthusian model failing to offer an explanation for why it no longer seemed to apply.

Conversely, modern economic theories such as the neoclassical growth model and endogenous growth theory, while being useful in understanding some elements of economic growth, do a poor job of explaining the nature of the Malthusian state that existed for most of human history.

Unified growth theory seeks to overcome the limitations of these approaches by presenting a coherent, single framework that captures the Malthusian era, the transition to higher growth and the modern growth state. The process of moving from one state to the next originates within unified growth models, with the seeds of the transition growing during the Malthusian era. As such, the Malthusian state is not an equilibrium, but a dynamic process leading to its own end. The benchmark for unified growth models is that they capture the patterns in income, technology and population through these various states and generate the transition between them.

Unified growth theory was first proposed and has largely been developed in the work of Oded Galor, who with his co-authors has put together the building blocks of the theory. In his book Unified Growth Theory, Galor catalogues his work in the area and demonstrates the strength of the foundation he has built.

The book is an academic book that Galor has largely constructed from his published papers. Some chapters are heavy on the mathematics, although Galor is a clear writer and it is possible to get a sense of unified growth theory without working through the models. The first chapter, in which Galor works through some of the core features of economic history, would provide a useful grounding for any intelligent lay reader, as would his discussion of the causes of the demographic transition.

I have posted about some of the papers that form book chapters before, so I won’t give a blow-by-blow account of the book. And if you are familiar with his papers, you won’t find many surprises. But what becomes clear from reading this work in a single collection is how coherently Galor’s work fits together (and that is from the perspective of someone who is skeptical of unified growth models as potentially trying to explain too much). While a couple of the chapters present full unified growth models, other chapters examine in detail particular elements of the theory. Galor examines the relationship between population, income and technology in the Malthusian state. He steps through the triggers of the demographic transition and examines which causes are plausible. He examines the accumulation of human capital across populations and how this coincides with changes in growth (the accumulation of human capital in response to technological progress is the core driver of the transition in Galor’s models). Put together, the case the Galor is building becomes clear.

In past posts I have focused on Galor’s work examining the interaction between evolutionary factors and economic growth. These evolutionary considerations are among the more peripheral parts of unified growth theory. Unified growth theory could survive without them. But what Galor emphasises is the range of theories that can be accommodated within a unified growth framework. For example, the effect of genetic diversity on innovation and cooperation could be taken to affect the population’s ability to accrue human capital. This then generates the divergence in economic outcomes within the unified growth framework, as one population accumulates enough human capital for a take-off in growth before the other. In this context, unified growth theory does not explain every facet of economic growth, but provides a framework under which much analysis can occur.

That said, it will be interesting to see which elements of Galor’s unified growth models stand the test of time, even if unified growth theory itself becomes a more broadly used approach. What is the nature of the trade-off between quantity and quality of children? What were the evolutionary changes during the Malthusian state? How do the models stand up when we examine specific questions under their lens, such as asking why England and not, say, China first experienced the take-off in economic growth? There is a lot of potential to put more flesh on the framework of unified growth theory and the models that Galor has developed within it.

So for those economists interested in the deep causes of economic growth, I would recommend Galor’s book, even if you are generally familiar with his work. Having that body of work systematically laid out in one piece gives it a strength not apparent when each part is taken alone.

And for those who are interested on some of my earlier posts on Galor’s work (with the corresponding book chapter in brackets):

1. Dynamics and Stagnation in the Malthusian Epoch (chapter 3)
2. The Neolithic Revolution and Comparative Development (chapters 6.4.1)
3. The “Out of Africa” Hypothesis, Human Genetic Diversity, and Comparative Economic Development (chapter 6.4.2)
4. Natural selection and the origin of economic growth (chapter 7)
5. Evolution of Life Expectancy and Economic Growth (chapter 7.7.2)

After sitting in my reading pile for the best part of three years, I have finally read (or more accurately, listened to) Dan Ariely’s Predictably Irrational. One of the most commonly referenced popular books on behavioural science, it describes Ariely’s experiments in the areas of cheating, procrastination, social norms, hot decision-making and so on.

One nice element of the book is that Ariely describes his own experiments. In the behavioural science literature, you often come across the same experiments over and over. The field sometimes doesn’t feel very deep. But it is easier to deal with this when the person is describing their own experiments (you can’t hold it against an author that others keeps referring to their work) and they are able to give the experiments some colour beyond the content of the published papers. Then again, if you have listened to a lot of Ariely’s lectures via podcast (as I have), much of the book will be familiar territory.

Rather than review the book, I thought I’d point out a couple of interesting ideas that Ariely presents.

First was his framing of how the link between price and demand flows in two directions. People do not simply demand a certain quantity at a certain price, as the price may feedback about how desirable a product is. Ariely opens that section with the story of how James Assael created demand for black pearls. After initially failing in his marketing attempts, he placed the pearls in the store of a gemstone dealer friend with an outrageously high price relative to the previous prices at which he had failed to sell any of the pearls. Accompanied by an advertising campaign, the demand was born.

Ariely’s explanation of this feedback between price and demand relates to our need for arbitrary coherence. Since we do not know what many things are worth, we will seek an anchor – say a previous price or the last two digits on our social security card. Once that anchor is created, this is the benchmark against which value is measured. Placing the pearls in the gemstone store and advertisements created an anchor that still exists.

A second interesting thread was the desire for individuality. Ariely relates an experiment where he offered a selection of beers to pub patrons as free samples. Where people ordered in sequence and aloud so that each member of a group heard what the others ordered, they tended to order different beers. With private ordering they tended to cluster more. This occurred because those who ordered last avoided the beers that people in their group had ordered before them. So, if you feel a need to express individuality through your meal or drink order, get in first.

Finally, Ariely’s work on hot decision-making, where his team provided computers loaded with arousing pictures to experimental subjects, is truly amusing.

All up, Predictably Irrational is not a bad book for a sample of behavioural economics if you are new to the area. And when you are done with it, I suggest balancing it with some Gigerenzer.