Soham Phade

Introduction to Behavioral Network Economics

We will mainly be concerned with the study of social systems comprised of several individuals, typically humans, henceforth called players, interacting directly or indirectly in a bounded situation (or an environment). Systems influenced by technological innovations over the past several decades will be of particular interest to us. For example, these include transportation and communication networks, the Internet, computation networks and data-centers, energy and utility networks, financial networks, labor markets, social networks, and digital markets.

The complex nature of these systems requires consideration of several crucial aspects which gave rise to the interdisciplinary fields of cybernetics and systems science. These combine knowledge from various fields such as control theory, information theory, dynamical systems, operations research, computer science, systems engineering, economics, statistics, and psychology. The engineering approach towards solving these problems primarily focuses on the physical aspects such as feasibility, practicality, maintainability, stability, and scal- ability. An equally important dimension is that of catering to individual preferences and needs. Ultimately these systems are there for the users. Thus enters marketing research and business management. These fields study the market economy and business processes to identify, anticipate and satisfy customers’ needs and wants. A holistic approach that combines these two approaches will go a long way.

Technological advancements in domains such as the Internet, Computing, Communica- tion, and Artificial Intelligence (AI) have lead to rapidly evolving network services such as cloud computing, smart information systems, multimedia platforms, software companies, online marketplaces, and smart grids, that have global scopes. Consequently, network eco- nomics research evolved along two major lines:

  1. Optimal routing and control: This involved the study of flow dynamics and congestion based on the underlying network structures and routing decisions. Typical problems studied include the shortest path problem, the maximum flow problem, the minimum cost flow problem, etc. (See books by Anna Nagurney [94, 95, 97, 98, 96].)

  2. Network formation and growth: Here, the focus is on the understanding of the for- mation of network links, the flow of information in social networks or diseases in epi- demiological studies, connectivity and segregation in different networks, etc. Models from random graph theory and statistics are helpful in this approach. (See books by Mathew Jackson [62, 63] and Sanjeev Goyal [54].)

Besides understanding the working of networks, a fundamental goal of network economics is to assist decision-making for both the system designer and the players in the system. For example, Braess’ paradox warns a network planner of the following counter-intuitive effect: adding additional links to a network can reduce the overall system utility (such as the total delays for all the drivers in a transportation network) at Nash equilibrium when each player is making an optimal self-interested decision. Observations like these and results from network economics have greatly helped policy-making and system design. (Shapiro and Varian [122] describe strategies to guide business decisions and policies in network economies such as differential pricing, utilizing network positive externalities and lock-in effects, patents and rights management, and others.)

Game theory and economics offer valuable guiding principles in the design of these sys- tems. The economic models for studying these problems typically assume that the partic- ipating agents are rational and possess immense computational power (which is reasonable when the participating agents are firms or nations). However, for e-commerce platforms like social media and online marketplaces, where the participating agents are single individuals who perform several repeated short-lived interactions with the platform, it is unusual that these agents would adhere to the above behavioral assumptions. We cannot expect the human mind to make informed and well-thought decisions in such complex interconnected systems, let alone the stress it generates. Our goal here is to use sophisticated models from behavioral psychology and decision theory to model human interaction and design robust and scalable systems that would assist the users in making decisions that are in their own interests and also for those around them.

The digital revolution has given rise to software companies having massive control over several crucial networks with the power to micromanage them. The algorithms deployed by these companies can influence social, economic, and political networks like never before. Along with all the evident benefits of these software systems in automating tasks and facil- itating large-scale network operations, we must pay closer attention to how these systems interact with their users. The growing human-computer interaction requires careful con- sideration of human behavior and their emotional responses. Our knowledge regarding the guiding principles for governing these interactions is quite limited, and a methodological approach towards incorporating psychological aspects into system design is barely off the ground. There is an ongoing debate relating to the benefits of these big technology com- panies, the extreme power these companies hold, and whether they are using it wisely or not. Although it will not be the focus of this thesis, I hope that the behavioral foundations developed in this work would help answer some of these questions (see Section 7.3), and consequently, help build systems that are better aware of human behavior and needs.

"Perhaps the most apt historical model for algorithmic regulation is not trust- busting, but environmental protection. To improve the ecology around a river, it isn’t enough to simply regulate companies pollution. Nor will it help to just break up the polluting companies. You need to think about how the river is used by citizens—what sort of residential buildings are constructed along the banks, what is transported up and down the river—and the fish that swim in the water. Fishermen, yachtsmen, ecologists, property developers, and area residents all need a say. Apply that metaphor to the online world: Politicians, citizen- scientists, activists, and ordinary people will all have to work together to co- govern a technology whose impact is dependent on everyone’s behavior, and that will be as integral to our lives and our economies as rivers once were to the emergence of early civilizations."

—Anne Applebaum, “The Internet doesn’t have to be awful.” The Atlantic. April 2021.