Example Applications of Behavioral Network Economics
Transportation Networks
Let’s say you want to reach the airport to catch a flight. You open a navigation app, such as Google Maps or Apple Maps, and check for possible routes and the estimated times of arrival. Your topmost concern is to arrive at your destination in time. Plus, you’d like to have a good estimate of your arrival time. Compare it with someone who might be using the same app but is looking for a scenic route and not so worried about his arrival time. At any given time, hundreds of thousands of users are using such apps to find what suits them the best. All these different people have varied requirements based on their purposes and preferences while sharing the same infrastructure and resources. The app recommendations affect their choices, and their choices have externalities that affect the conditions for others. One could imagine the app providing signals and economic incentives to alter traffic patterns.
A familiar example in this spirit is clearing the way for emergency vehicles. Something that we have been doing for several years. Another example is charging a variable rate adapted to the traffic conditions for the use of the express lanes. Given the prevalent use of navigation apps and other communicating devices today, we have more options to influence traffic routing. At the same time, we can collect and process a lot more data. Our goal is to explore ideas along these lines. An important thing to notice here is that the players in this system are human agents and they are bound to display behavioral features that do not fall under the traditional notions of rationality. For example, drivers might prefer routes that they are familiar with, even if the alternative route is faster. (This is reminiscent of the well-documented endowment effect, which says that people are more likely to hold onto an object they own rather than trade it for an equally or higher valued alternative they do not own. The fear of the unknown and uncertainty also plays a role here.) We must incorporate these behavioral features into system modeling. Furthermore, this applies to all forms of transportation services such as public transport, railways, airways, waterways, shipping of goods, etc.
Communication Networks
Using navigation apps to help route traffic is just an instance of taking advantage of the advanced communication technologies for improving resource allocation. Indeed, communi- cating the availability of resources, individual preferences, and incentives for resource man- agement, and controlling system parameters require real-time information transfer and sig- naling. No wonder the Internet was the first to witness real-time algorithm-based traffic management. Transmission Control Protocol (TCP) and bandwidth allocation algorithms have helped avoid the congestion issues that had plagued the Internet before TCP. The the- oretical foundations for this work were laid by Kelly in the late 1990s [72, 73]. In Chapter 2, we extend these ideas to incorporate behavioral features and psychological traits displayed by the users.
Today, traffic shaping is a major area that deals with congestion control [84, 116]. The users are allocated bandwidth based on the choice of the monthly plans selected by them and the ambient network traffic conditions. One of our goals is to extend these ideas to real-time traffic management. For example, imagine you have a virtual presentation coming up. It would be nice to indicate this to the service provider, such as Xfinity or AT&T, and request a boost for this period. It might result in additional charges, but it would provide you the added benefit of choosing a more economical base plan. Certainly, re-engineering the Internet along these lines would increase user-system interactions and it would need algorithms that are more aware of human behavior and responses.
Cloud Computing Networks
Just as communication networks allocate bandwidth to the users, cloud computing networks, such as Amazon Web Services, Microsoft Azure, or Google Cloud, provide on-demand com- puter system resources such as data storage and computing power. Cloud service providers can schedule most of the customer jobs instantly today as the resources exceed the demand. However, with a growing trend of customers opting for computing resources as a service instead of maintaining such systems on their own, this surplus luxury is not sustainable. Resources are also naturally constrained in settings such as fog computing and peer-to-peer computing networks. Besides, concerns over the energy consumption by data centers is another factor that limits the expansion of computing resources.
The demand for resources can vary significantly over time, different jobs have different resource requirements, and customers have varying preferences towards their job delays and the quality of service. The prices must conform to these changing demands in real-time. Although the typical customers in this setting are firms and organizations, the end-users of their services and products are often individual humans. The value and revenue gener- ation for these organizations is closely related to the levels of consumer satisfaction. As a result, behavioral considerations naturally creep into the utilities and preferences of these organizations.
Energy Networks
Smart grids are another excellent example of the application of digital processing and com- munications to systems where user interactions play a major role. The goal here is to improve the economic efficiency of electricity networks and maintain high levels of quality of supply by integrating the behavior and actions of all the users connected to the network - genera- tors, consumers, and those that do both. It would provide communication protocols to the suppliers and the consumers, allowing them to be more flexible and sophisticated in their operational strategies. For example, the suppliers could indicate their energy prices, and the consumers could indicate their willingness to pay in real-time. The users can configure smart devices to generate additional energy or initiate energy-saving modes under specific settings such as during high-cost peak usage periods. Similar to the pricing based on job delays in the cloud computing setting, we can imagine customers having different prefer- ences towards their energy requirements based on deadlines, for example, such requirements would naturally occur in charging of electric vehicles. Today, PG&E, a utility company that provides natural gas and electric service, offers different pricing schemes such as time-of-use rate plans and tiered usage rate plans. Along similar lines, we are interested in much more flexible and sophisticated pricing schemes based on dynamic market conditions and human behavior analysis. This would also benefit in incentivizing people to shift to clean electricity options and adopt solar panels at home.
Social Networks
Several activities such as advertising, campaigning, or running welfare programs depend on the underlying social networks. Humans are the primary agents in any social network. Their interactions and behavior form an integral part in the study of social networks. Models that incorporate psychological aspects are needed to better allocate resources in these activities. It would help answer questions like: How can we maximize the impact of a campaign with a limited budget? How to best incentivize the agents in a network to perform actions that are in the best interests of the entire society?
From a commercial point of view, it would greatly benefit the online ad exchange com- panies such as Google Ads or Facebook Ads. These are digital marketplaces that enable advertisers to buy and sell advertising spaces. Here, user attention is the limited resource and the different advertisers are competing for this limited resource. The tools developed in this thesis will help regulate these markets more efficiently by incorporating human behavioral features.
Matching Markets
Just like the ad exchange marketplace, several other matching markets fall in this domain. These include labor markets that match employers and workers such as Upwork and Free- lancer, ride hailing applications that match drivers and riders such as Uber and Lyft, delivery services that match restaurants and diners such as Doordash and UberEats, or online mar- ketplaces that match sellers and buyers such as Amazon and eBay. Notice that most of the participating agents in these settings are individual humans susceptible to showing behavior that is influenced by biases and heuristics.
Finance and Insurance
Finance and insurance is another interesting setting where behavioral factors play a huge role. There is a decent amount of work studying how individuals make decisions about their investment strategies and insurance policies, but there is only a limited amount of work that considers behavioral features in a financial network setting where the individuals interact with each other and their decisions affect the other individuals in the network. In this work, we establish results that would facilitate this research.
Observe that, in all the above examples, the following factors are common:
- The resources are limited.
- Players have varying requirements and preferences.
- The preferences of the players are private information.
- Players have limited information about the system operations and constraints. 5. Players show behavioral features.
The goal is to design a communication protocol or a market system to fa- cilitate the exchange of information for strategic players who might display behavioral features, and consequently allocate resources to satisfy certain re- quirements. In contrast to prior works, we will pay special attention to the last factor, namely, the behavioral features of the players. We aim to bring these aspects to the same level of mathematical sophistication as other aspects in system sciences. Such an approach is crucial to building systems that are scalable across different users and robust to the intricacies of human behavior.