Behavioral Economics — Introduction and Origin
While we think the decisions we make are rational given the circumstances, more often than not, our decisions are subject to many biases and heuristics. Explaining the disunities between expected rational and actual behavior is what Behavioral Economics seeks to accomplish.
2020 has been quite an eventful year, it’s only the third month and it seems that the world is more volatile than ever before. I’d like to present a quick introduction to the subfield of behavioral economics and its origin. In a follow-up article, I would like to build off of this and demonstrate examples observed in recent news for 2020.
An introduction to the history behind Economics
The fundamental units that compose Economic theory are humans and their respective behaviors as individuals and aggregations. It began with the philosophical and qualitative economic theories of Adam Smith to the shift of the mathematization of economics in the mid 19th century. This started with simple algebraic methods then branched into the Calculus and more as it became evident the mathematics of change(Calculus) and more quantitative methods to describe behavior were necessary and possible since data began to be collected and saved. While the tools and methods changed, the goal to model behavior and interactions of people within economies has not changed.
Classical Economic theory builds on a simple but powerful model of human behavior: Individuals make choices so as to maximize their preferences, using all information available and processing this information appropriately. There is an assumption that preferences are stable or rational. We call preferences that are complete and transitive rational. An implication of this is the abstract notion of utility functions, a function that attempts to estimate a numerical value out of preferences. We infer that people are always expected utility maximizers, or always try to gain the most satisfaction out of every decision made.
In any Microeconomic Theory textbook, the first thing you will see is 3 axioms of consumer preferences, Completeness, Transitivity, and Continuity. I won’t dive too into detail but essentially completeness ensures that you can compare any set of objects and make a decision, there is no in-between. An example: A person prefers the beach to a park, a park to the beach, or likes them both equally in which they are indifferent between the 2 places. Transitivity, which is subject to controversy states that if somebody prefers option A over B, option B over C, then they must prefer option A over C. In canonical form, this can be represented as A>B → B>C →, therefore, A>C.
The birth of Behavioral Economics
It’s not to say behavioral elements and insights from psychology were not considered by classical economists, in fact, Adam Smith wrote about it in his Theory of Moral Sentiments which considered factors that affect behavior such as fairness and justice. I also remember reading a paper called “Fairness and Assumptions of Economics” by Kahneman, Knetsch, and Thaler in my Negotiations class during University. A relevant proverb goes,
“The past does not repeat itself, but it rhymes”
In the late 19th century, cognitive psychology started to reveal more insights about the brain and its decision making processes. Amos Tversky and Daniel Kahneman were the pioneering psychologists who began comparing their models of decision-making under certain contexts to classic economic models(remember the assumptions of rational behavior). It is these models combined with Herbert Simon’s criticism of the assumptions in traditional economic models that gave rise to Behavioral Economics. While one argument from the classical side was that those models were easier to standardize than psychological models, the inaccuracy in explaining observed behavior was too much to deem the classical explanatory models.
“Bounded Rationality”, an intuitive idea that when people make decisions, there are constraints to their rationality such as the space of possible decision paths(Think Decision Trees), cognitive limitations and time. Hence these variables influence our decision making so as to be satisfactory given our conditions or purposes, which means we don’t necessarily make the optimal decision because it could be out of our bounds of knowledge.
A related quote from an essay I read by Freedom in Thought. The link is here
” The reason for any single decision you make is multivariate; genes, hormones, evolution, social and physical environment, past experience, context and a multitude of other factors”.
Side note: A nice touch I want to add that is relevant to this quote for further reading if you are interested are branches of Economics related to Behavioral Economics called Genoeconomics(Using genetic data in economics) and Neuroeconomics(Using Neuroscience data in economics). To learn more I recommend looking up Dan Benjamin, Ernst Fehr, and David Laibson. In addition, Caltech, MIT, Berkeley, and USC’s labs in Neuroeconomics.
To solve the optimization problem, humans naturally developed mental shortcuts, heuristics, and biases to facilitate decision-making. After all, who has the time to be on the extreme end of optimal decision making. Imagine calculating the opportunity cost and utility for every outcome…right, that’s not what normal humans do. Unless you’re the Homo Economicus agent classical economics assumed you to be, which I highly doubt. Discovering and understanding these shortcuts allows behavioral economists to increase the efficacy of human decisions for a wide array of applications such as public policy, human decision-making, marketing and more.
Behavioral Economics began to blossom, concepts like Bounded Rationality and Prospect Theory became established. Prospect Theory is composed of many behaviors and traits, examples include loss aversion, reference points, overconfidence, projection bias with discounting future events/consumptions, and limited attention to information. I plan on starting a series of articles that dive deeper into these insights and covering the academic literature associated with them.
Using these insights, behavioral models were created to explain the confounding behaviors at the individual and aggregate level as they diverged from the expectations of classical economic models.