Behavioral economics is an approach to microeconomics that uses experiments to determine how agents make choices within an economic context, it studies the effects of psychological, social, cognitive, and emotional factors on the economic decisions of individuals and institutions and the consequences of this to broader economic outcomes.1 Behavioral models typically integrate insights from psychology, neuroscience and microeconomic theory; in so doing, these behavioral models cover a range of concepts, methods, and fields that is not restricted to standard economic theories. Behavioral economics has grown as an alternative approach to standard economic theory pursuing more experimental, data-driven methods, without strong association to more traditional theoretical models.
Standard microeconomics is based on the model of the so-called “rational agent.” The agents have unlimited rationality, the idea of omnipotence, that is to say, they know everything, and can compute all the consequences. Within this model, agents have perfect information and any uncertainty can be reduced to a simple probability distribution. The agent’s behavior then will be a simple optimization algorithm over their set possibilities, and it is thought that behavior can be altered by changing the input variables to this optimization algorithm by what we call positive incentive.2
Behavioral economics allows for more social, cultural and environmental factors within this paradigm value becomes a more complex multi-dimensional thing with agents often making trade-offs between different types of value and never fully sure about the value of things or their preference. From this perspective people’s rationality is bounded, meaning they can only think so much. They always exist within a context of space and time and are strongly limited by that particular context. Information is often incomplete and radical uncertainty may exist in outcomes. Due to all of this, agents will use all sorts of heuristics and shortcuts in order to make decisions on incomplete information with limited cognitive capabilities. From this alternative vision of behavioral economics, we get a very different answer as to how to design and build incentive systems, one that is less focused on altering the payoff to individuals and more focused on altering the context within which agents are making their decisions.3
Standard economics uses an extrinsic theory of value. This concept of value is derived from the revealed preferences of agents. Standard economic agents have clear preference. This preference reveals their values. In choosing one thing over another, the utility of that will be revealed to the economist. In economics, utility is a measure of preferences over some set of goods and services. Because utility is always defined through revealed preference, it always exists with respect to someone or organization. Objective value is then defined through the interplay between people’s utility functions, that is to say, the interaction between producers and consumers trying to maximize their utility creates what is called a market, and the market defines the economic value of something, what we call its price. Thus, out of people’s subjective evaluation of things we have managed to create an objective value for some commodity, but this value only exists in relation to people’s willingness and capacity to pay for it.4
Behavioral economics is interested in the opposite phenomena, i.e. all the cases whenever the concept of extrinsic value breaks down and we see that value can be created by the context, that is to say, independent from the properties of the good. Much empirical data from behavioral economics has shown that people’s evaluation of things is framed by the context, and this context involves many social, cultural or even environmental factors, all of which can add or subtract value to a good, service or activity. Thus, in order to capture this concept of intrinsic value, we need a much more complex multidimensional conception of value, one that incorporates all of these facts. It includes social capital, cultural capital, industrial capital and ecological capital all rolled up into one.
Out of this more complex conception of value, we get something that is able to approximate the idea of well-being, in that we know that well being is not captured in a single value such as GDP, but in fact is a much more subtle thing that emerges out of one’s connections with the things that one values – friendship, sense of purpose, respect from others, security, health etc. all of which this more complex metric of intrinsic value tries to capture. What behavioral economics and the empirical data coming out of it have shown, not surprisingly, is that people are in fact people. They are complex creatures. They do not just value one thing. They value lots of different things. What people really want is well-being, and well-being is not just one thing. It is created out of the interaction between many different things and it changes depending on the person and the context.5
Standard economics sees choice as an optimization algorithm over a set of well-defined options that remain unchanged by the context. This is based upon the idea of consistent choice. If you choose one thing over another now, then you should always choose that thing over the other independent from other factors that are exogenous to this equation. When making choices, agents are seen to be simply computing the results to an equation and choosing the maximum payoff.
Part of the rational agent model is the idea of complete or perfect information, that is to say, agents have complete information of costs and payoffs to all options that are available and they are able to compute all of these payoffs. Of course, everyone recognizes that only very simple situations will have explicit values associated with all options. In many situations, the values associated with costs and payoffs are not explicit. They are contingent on other events and how thing play out over time. In such a case, the rational model uses probability and statistics to ascribe a well-defined value to these unknown variables. An assumption built into this is that we can take a sample from the past and project it onto the future. It is an assumption that the past and the future on aggregate are the same. Another assumption here is that the actions of an individual agent are on aggregate the same as the average of the entire population.
Some choices such as choosing which song to purchase on iTunes may involve millions of different options, and also many choices that agents face are dynamic, meaning they will unfold over time. The choices we make now will affect the choices we make tomorrow and so on, as the possibilities branch out into the future because this is a tree graph. The number of options and associated payoffs typically grows exponentially. The net result is that we will a need massive amounts of computing power if we want to try and calculate closed form solutions for many real world choices. Standard models ascribe this computational capability to agents, not because anyone really believes that – that is how we are – but instead because it is necessary to get these closed form solutions. Within this model the human’s cognitive functioning is seen to be very much comparative to that of a computer, simply running logically consistent optimization algorithms over a well-defined database of options, with systematic logical inconsistencies thought to be impossible.
The model that behavioral economics presents us with, and what comes out of the data is a very different picture. Here the value of something is very much contingent upon its context and framing. From this perspective, human beings have very limited capabilities for logical deliberative reasoning. Behavioral economics draws upon neuroscience and evolutionary biology to present a picture to human decision making that is driven much more by irrational instincts, primordial motives such as hope, fear, and greed, that all totally bypass any kind of abstract isolated rational reasoning based on objective information.
Agents are driven by motives and these motives frame our whole point of reference. So if your care, motivation or fear is activated, then you will interpret information through this context. You will interpret signs differently, seeing cues that symbolize these things more readily. Motivation organizing your perception is very different from the computation model to how humans interpret and process information. This agent with limited cognitive capabilities is placed in the world with a single location at a single point in time. In this scenario, information is scarce. Agents may only have access to local information and the future represents a deep uncertainty. With all of this lack of information and incapacity to process it all, we use all sorts of shortcuts that allow us to cope in complex environments. We make many irrational associations between things that aren’t always apparent. We make reference to the context and our environment, such as simply copying other people. We think in scenarios and narrative. Everything has to fit into a context for us to make sense of it and that context can manipulate the meaning and value of anything within it.6
Until recently, it was very difficult to mathematically model systems that have many components with each of those components having many degrees of freedom, what we call a complex system. All we really had was things like differential equations, vector fields and basic statistics and probability. These tools were designed for computing the trajectory of planets around the sun or fluid dynamics. They weren’t really designed for this application. Thus, our economic models were always trying to accommodate this lack of basic tools. Many standard economic models to the behavior of agents will look austere with simplified assumptions. This is because they are trying to encode complex phenomena into traditional tools, many of which were invented some three or four hundred years ago.
Today, we have new tools based on computation such as agent-based modeling and nonlinear iterative maps. These computational models can handle massive amounts of information, the kind that Sir Isaac Newton could only have dreamt of. From them, we can get much richer models that do not require these very simplified reductive assumptions of standard economics. They will allow us to paint a much more complex and subtle picture to the values, motives and real world behavior of agents. But in order to do this, we need a basic understanding to the behavior of agents, some explanation to all of the key consideration. And this is what the new area of behavioral economics is tackling.
Traditional models dealt with very generalized aggregations, due to lack of information, they could not say what people were actually thinking or doing. With the availability of a mass of new data sources from social networks and the internet that we now have access to, we can get much more personalized models specific to each individual, possibly even in different unique situations, again enabling new models that are not so dependent upon very austere abstractions of generalized behavior.