 In this section to the course, we'll be turning our attention to what insight and models complexity economics can offer us in trying to understand the basic building blocks of economics that is to say people or agents. Agents are so called because they have agency, which means a thing or person that acts to produce a particular result. The basic premise of economics is that people have some conception of what they value. They will try to be efficient in the expenditure of their resources in order to achieve these valued ends, what is called economizing. And they will respond to external interventions called incentives in order to try and achieve these ends. These agents in the course of doing their activity of economizing will have to make choices. Thus, we're going to need some account to all of these things. That is to say, what do people value? How do people make choices? And how do they respond to incentives? In this video, we'll be exploring two very different models given to capture this. We'll talk about how standard economics offers this model of the rational individual, sometimes called homo-economicus. And we'll draw upon the new area of economics called behavioral economics that presents very much an alternative model to human behavior within an economic context. In many ways, the divide between standard economics and behavioral economics is a divide within economic science between theory and empirical data. On the micro level, standard economic theory has never really been subjected to empirical data. Models derived from classical physics got mathematicized during the 20th century into very quantitative, abstract and theoretical representations of human behavior. With limited reference to empirical data, the emphasis was on formalizing equation-based models. Empirical data was seen to be somewhat redundant. Over the past few decades, researchers in neuroscience, psychology and new areas of economics have started to conduct experiments, and it's turned out that the data coming from these experiments doesn't fit very well with the standard model. In order to try and describe what is coming out of the data, behavioral economics has grown as an alternative approach pursuing more experimental, data-driven methods without reference to the more traditional models and some very interesting insights have come out of this. Complexity theory is very well suited to supporting this new fledgling approach to microeconomics, as we'll see much of this empirical phenomena can be best described with reference to nonlinear models. Standard microeconomics is based on this model of the so-called rational agent. The rational agent exists in the abstract. That is to say, nothing in the model is contingent on context. Space and time do not exist in these models. Agents have a single conception of value. That is to say, all value is reduced to a single homogeneous form called utility. Within this model, preferences and value are well defined. The agents have unlimited rationality, the idea of omnipotence. That is to say, they can know everything and can compute the consequences of everything. 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 of possibilities and it's thought that behavior can be altered by changing the input variables to this optimization algorithm by what we call positive incentives. Behavioral economics is going to give us some very different answers to these questions. By allowing for more social, cultural and environmental factors, value will become a much 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 preferences. In 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. You can only spend so much time thinking about which cookies you want to buy in the supermarket. You don't have your whole life to do this and you don't have a supercomputer to help you. 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 will get a very different answer as to how to design and build incentive systems. One that is less focused on altering the payoffs to individuals and more focused on altering the context within which agents make their decisions. So this is a very quick overview to the two different models to economic agents. The first, which is very much theoretical. The second, which is very much empirically derived. We'll now briefly go over the main differences between each. Firstly, all models to economic agents are going to need some description to what value is because at the end of the day, this is all about people trying to achieve or obtain the things that they value. There are really just two theories as to where value comes from. Either it is intrinsic to something, that is to say independent from anyone's evaluation of it, or value is extrinsic, that is to say it is given to the entity by the evaluation that some agent places on it. Standard economics uses an extrinsic theory of value. This concept of value will be derived from the revealed preference of agents. Standard economic agents have clear preferences. 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 preference over some set of goods and services because utility is always defined through revealed preference. It always exists with respect to someone or some organization. Objective value is then defined through the interplay between different 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. And thus, out of people's subjective evaluation of things, we've managed to create an objective value for some commodity because this value is extrinsic, it only exists in relation to people's willingness and capacity to pay for it. As you might imagine, behavioral economics is interested in the complete opposite, that is to say all the cases when 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. And thus, in order to capture this concept of intrinsic value, we need a much more complex, multi-dimensional concept of value one that incorporates these factors, it includes social capital, cultural capital, industrial capital, ecological capital all rolled into one. Out of this more complex conception of value, we get something that is able to approximate this big 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 that's come out of it have shown, not surprisingly, is that people are in fact people they are complex creatures as we might expect, they don't 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 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 things play out over time in such a case the rational model uses probability and statistics to describe 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 these assumptions only really hold in closed linear systems and it's called ergodicity 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 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 need a massive amount 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 humans 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 the behavioral economics presents us with and what comes out of the data is of course 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 upon objective information agents are driven by motives and these motives frame our whole point of reference so if your care motive or fear motive are activated then you will interpret information through this context you will interpret signs differently seeing cues that symbolize these things more readily motivation organizing our perception is very different from the computational 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 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 in our environment such as simply copying other people we think in scenarios and narratives everything has to fit into a context for us to make sense of it and the context can manipulate the meaning and value of things within it finally we'll talk a bit about the context surrounding this current debate between the rational model and the behavioral model until recently it was very difficult to mathematically model systems that had many components with each of those components having many degrees of freedom what we might 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 calculating 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're 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 have only dreamt of from them we can get a much richer model that doesn't 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 considerations that we've been discussing in this video 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 networking and the internet we can get much more personalized models specific to different individuals and even in different unique situations again enabling new models that are not so dependent upon very austere abstractions of generalized behavior part of the problem today is that very few people understand the nonlinear computational methods that are required to support this new approach and thus people stay coming back to this either or situation where either we use formal methods that are known to be limited but are tractable or accept the data and lose the traction when in fact we increasingly have the computational models and methods to formalize this data it's just very much under development as of yet in this module we've been giving a short overview to behavioral economics and how it differs from our standard model of the so-called rational agent we mentioned how they represent two approaches one very theoretical the other very empirical with both coming to very different conclusions about how agents value things and make choices within the rational model value is defined extrinsically in terms of utility agents have well-defined unchanging preferences perfect information and making choices is then simply an optimization process behavioral economics sees value as being more intrinsic as it is contingent upon the context agents exist in a complex world always embedded within space and time with limited information and cognitive capabilities often facing radical uncertainty and in the face of this their choices are more a product of how they are contextualized and framed both physically socially and culturally as they imitate others use heuristic shortcuts or create narratives to aid them in their decision-making process