We don’t have a hundred biases, we have the wrong model

Key takeaways §

• Behavioral economics needs a new model rather than more biases compared to a rational actor
• A new model for decision making needs to focus less on bias, be realistic, be environment specific, and multidisciplinary
• The first is a weakened focus on the concept of bias. The point of decision-making is not to minimize bias. It is to minimize error, of which bias is one component. In some environments, a biased decision-making tool will deliver the lowest error.
• Second, the human mind is a computationally constrained resource. Even if optimization is the best approach – and it often isn’t – the best we can usually do is approximate optimization. The decision-making rule needs to be feasible with the mind we have.
• The third feature is that the outcome of a decision is the combination of the decision-making tool and the environment in which it is used. The polymath Herbert Simon described rationality as being shaped by the two blades of a pair of scissors. One blade represents the structure of the environment, the other the computational tool kit of the decision maker. You need to examine both the tool and the environment to understand the nature of the decision that has been made.
• Finally, any successful heliocentric approach to modeling behavior will have a fourth feature: It will be multidisciplinary. It won’t involve economics picking up a couple of random pieces of psychology. We will find insight across the sciences.

From a predictive point of view, you have a range of countervailing biases that you need to disentangle. From a diagnostic point of view, you have an explanation no matter what decision they make. And if you can explain everything, you explain nothing.

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Highlights §

• The result was a complicated pattern of deviations and fixes to this model of the sun, planets, and stars orbiting around the Earth.
• By adopting this new model of the solar system, a large collection of deviations was shaped into a coherent model. The retrograde movements of the planets were given a simple explanation.
  • Note: Unifying theories that tie together many different observations are particularly strong.
• Behavioral economics today is famous for its increasingly large collection of deviations from rationality, or, as they are often called, ‘biases’. While useful in applied work, it is time to shift our focus from collecting deviations from a model of rationality that we know is not true. Rather, we need to develop new theories of human decision to progress behavioral economics as a science.
• This list of deviations has grown to the extent that if you visit the Wikipedia page ‘List of Cognitive Biases’ you will now see in excess of 200 biases and ‘effects’. These range from the classics described in the seminal papers of Amos Tversky and Daniel Kahneman through to the obscure.
• But there is something unsatisfying about this being the frontier of behavioral economics as a science. Dig into many of these applications and you see a philosophy of ‘grab a bunch of ideas and see which ones work’. There is no theoretical framework to guide the selection of interventions, but rather a potpourri of empirical phenomena to pan through.
• From a predictive point of view, you have a range of countervailing biases that you need to disentangle. From a diagnostic point of view, you have an explanation no matter what decision they make. And if you can explain everything, you explain nothing.
• The best minds have settled on a role closer to technicians or engineers.
  • Note: Ouff, this hit home. Important to keep some kind of theoretical depth not just being a fancy technician!
• The first is a weakened focus on the concept of bias. The point of decision-making is not to minimize bias. It is to minimize error, of which bias is one component. In some environments, a biased decision-making tool will deliver the lowest error.
• Second, the human mind is a computationally constrained resource. Even if optimization is the best approach – and it often isn’t – the best we can usually do is approximate optimization. The decision-making rule needs to be feasible with the mind we have.
• The third feature is that the outcome of a decision is the combination of the decision-making tool and the environment in which it is used. The polymath Herbert Simon described rationality as being shaped by the two blades of a pair of scissors. One blade represents the structure of the environment, the other the computational tool kit of the decision maker. You need to examine both the tool and the environment to understand the nature of the decision that has been made.
• Finally, any successful heliocentric approach to modeling behavior will have a fourth feature: It will be multidisciplinary. It won’t involve economics picking up a couple of random pieces of psychology. We will find insight across the sciences.
• For a start, it tells us something about our objectives. All your ancestors, without fail, managed to survive to reproductive age and reproduce. This does not mean that we assess every action by whether it aids survival or reproduction. Instead, evolution shapes proximate mechanisms that lead to that ultimate goal. For example, we crave the sweet and fatty foods that increased survival in ancestral times.
• Mismatch is a prospective frame for rethinking bias. Mental tools shaped in one environment may fail in a new context.
• Our tools for filtering information in small bands may not function as well in a world of social media.
• The benefit of understanding evolutionary objectives is richer than simply understanding the functional reason for a decision. It might enable you to understand the patterns of when a particular decision tool works or not. You can gain insight into what circumstances might evoke the behavior.
• Another field that may help build a new model of human behavior is computer science and, in particular, the development of decision-making and learning algorithms.
• Successful algorithms can be repurposed into hypotheses about how humans make decisions.
• This finding has had implications for research into happiness and hedonic adaptation and how these in turn affect behavior. If our mind uses a TD learning algorithm, it is not the level of the outcome that causes the positive feelings associated with success but prediction errors arising from exceeding expectations. This leads to a possible explanation for the centrality of reference points to Kahneman and Tversky’s prospect theory, whereby our utility is not a function of absolute levels but rather changes.
• With this framing, you can see the parallel with evolution and mismatch. Evolution ultimately rewards survival and reproduction, but we don’t receive a reward only at the moment we produce offspring. Evolution has given us proximate objectives that lead to that ultimate outcome, with rewards along the way for doing things that tended to (over our evolutionary past) lead to reproductive success.