Future of Behavioral Network Economics
EUT has been the backbone of almost every development in economics and game theory. It feels natural to extend these achievements to more general and better models of behavior if possible. CPT is an excellent tool to make progress in this direction. In my experience, CPT often introduces several modeling complications, however, in most cases, as is evident from this thesis, it is possible to extend the results from game theory and economics to players with CPT preferences. In each instance, we had to make special provisions either to the underlying framework or define appropriate notions - new ones or old ones with modified interpretations. This portrays the richness of this pursuit and a cue for future undertakings along these lines.
By no means this means that CPT can solve all the problems observed due to behavioral factors and deviations from EUT. For example, in [102], Nwogugu points out that “human beings and human decision making are subject to emotions, fairness considerations, ethical considerations, implied or actual constraints, personal aspirations, philosophical differences, regret, regret aversion, social pressure, peer pressure, phobias, perception of incentives, differ- ences in cognition, biological differences in neural activity, willingness to defer, reciprocation, and willingness to use risk management tools, all of which result in significant departures from rationality and traditional models of humans in decision making.” Some of these as- pects can be accounted for by CPT and some cannot. This doesn’t mean that we should abandon the approach based on theories of rationality. Indeed, principled approaches based on fundamental theoretical developments have often helped in guiding real-world applica- tions. Here is an excerpt from [118] stated in another context that illustrates the value of theoretical pursuits in applied fields:
Such an engineering approach is essential in behavioral economics too. Today, behavioral economics is often seen as a magician’s tool used to manipulate human choices and responses in contrast to the mainstream developments in economics and game theory. For example, one of the most frequently cited examples of a nudge is the etching of the image of a housefly into the men’s room urinals at Amsterdam’s Schiphol Airport, which is intended to “improve the aim.” Some of the behavioral economists have often expressed their discomfort in this approach. For example, David Gal, a professor of marketing at the University of Illinois at Chicago, says in an article that appeared in the New York Times with the title “Selling Behavioral Economics”: “There is nothing wrong with achieving small victories with mi- nor interventions. The worry, however, is that the perceived simplicity and efficacy of such tactics will distract decision makers from more substantive efforts—for example, reducing electricity consumption by taxing it more heavily or investing in renewable energy resources. It is great that behavioral economics is receiving its due; the field has contributed signifi- cantly to our understanding of ourselves. But in all the excitement, its important to keep an eye on its limits.” George Loewenstein, a professor of economics and psychology at Carnegie Mellon University and Peter Ubel, a professor of business and public policy at Duke and the author of “Free Market Madness: Why Human Nature Is at Odds With Economics,” say in an article that appeared in the New York Times with the title “Economics Behaving Badly”: “Behavioral economics should complement, not substitute for, more substantive economic interventions. If traditional economics suggests that we should have a larger price differ- ence between sugar-free and sugared drinks, behavioral economics could suggest whether consumers would respond better to a subsidy on unsweetened drinks or a tax on sugary drinks. But thats the most it can do. For all of its insights, behavioral economics alone is not a viable alternative to the kinds of far-reaching policies we need to tackle our nations challenges.”
In my view, behavioral economics need not be limited as an addendum, but instead we must try to bring behavioral economics to the same level of rigor as classical economics. In this work, we have just scratched the surface in this regard. Already we saw several benefits of using CPT to model the players’ behavior. For example, in Chapter 2 we saw the benefits of lotteries in resource allocation which cannot be explained by EUT. Furthermore, the fact that optimal resource allocation can be achieved in a market-based setting with real- time signals between the players and the system operator would enable its implementation in real-world scenarios. In Chapter 6, we saw the use of the messaging stage to recover the revelation principle. In this chapter, we will discuss few directions for future work where behavioral economics would play a huge role if applied in the spirit of the engineering approach mentioned above. Besides, to include behavioral features not explained by CPT, we will have to incorporate other behavioral theories (some of which exist today, and some which will be developed in the future). It is important that these behavioral theories have a nice mathematical formulation. By a nice mathematical formulation, I mean something that can be used to model, study, and develop applications and algorithms for social systems.
Until now in this thesis, I have mainly restricted to making concrete statements about abstract theoretical ideas. In this chapter, at the expense of making half-baked proposals or impractical claims, I will attempt to unwrap some of the theoretical insights developed in this thesis, and provide a version of how they could play out in real-world applications. Finally, to express my intention towards this thesis and the spirit in which this work and the work to follow should be interpreted, I would like to quote Professor Rummel from his 1975 book “Understanding Conflict and War, Vol. 1: The Dynamic Psychological Field,”