WISE16: Abstracts

Nageeb Ali (Penn State). "Laws, Norms, and Authority"

Itai Arieli (Technion). "Private Bayesian Persuasion" with Yakov Babichenko

We consider a multi-agent Bayesian persuasion problem where an in- formed sender tries to persuade a group of agents to adopt a certain product. The sender is allowed to commit to a signaling policy where she sends a private signal to every agent. The payoff to the sender is a function of the subset of adopters. We characterize an optimal signaling policy and the maximal revenue to the sender for three different types of payoff functions: supermodular, symmetric submodular, and a supermajority function. Moreover, using tools from cooperative game theory we provide a necessary and sufficient condition under which public signaling policy is optimal.

Kostas Bimpikis (Stanford). "Cournot Competition in Networked Markets"

The paper considers a model of competition among firms that produce a homogeneous good in a networked environment. A bipartite graph determines which subset of markets a firm can supply to. Firms compete in Cournot and decide how to allocate their production output to the markets they are directly connected to. We provide a characterization of the production quantities at the unique equilibrium of the resulting game for any given network. Our results identify a novel connection between the equilibrium outcome and supply paths in the underlying network structure. We then proceed to study the impact of changes in the competition structure, e.g., due to a firm expanding into a new market or two firms merging, on firms' profits and consumer welfare. The modeling framework we propose can be used in assessing whether expanding in a new market is profitable for a firm, identifying opportunities for collaboration, e.g., a merger, joint venture, or acquisition, between competing firms, and guiding regulatory action in the context of market design and antitrust analysis.

Yang Cai (McGill). "A Duality-Based Framework for Designing Simple Mechanisms" with Nikhil R. Devanur, Matt Weinberg and Mingfei Zhao

A central theme in mechanism design is understanding the tradeoff between simplicity and optimality of the designed mechanism. An important and challenging task here is to design simple multi-item mechanisms that can approximately optimize the revenue, as the revenue-optimal mechanisms are known to be extremely complex and thus hard to implement. Recently, we have witnessed several breakthroughs on this front obtaining simple and approximately optimal mechanisms when the buyers have unit-demand (Chawla et. al. '10) or additive (Yao '15) valuations. Although these two settings are highly similar, the techniques employed in these results are completely different and difficult to extend to more general settings.
In this talk, I will present a principled approach to design simple and approximately optimal mechanisms based on duality theory. Our approach unifies and improves both of the aforementioned results, and extends these simple mechanisms to broader settings.

Arun Chandrasekhar (Stanford). Social Networks, Reputation and Commitment: Evidence from a Savings Monitors Experiment" with Emily Breza

We study whether individuals save more when information about their savings is shared with another village member (a "monitor"). We focus on whether the monitor's effectiveness depends on her network position. Central monitors may be better able to disseminate information, and more proximate monitors may pass information to individuals who interact with the saver frequently. In 30 villages, we randomly assign monitors. Average monitors increase savings by 35%. A one-standard deviation more central monitor increases savings by 14%; increasing proximity from social distance three to two increases savings by 16%. The increased savings persist over a year after the intervention's end, and monitored savers better respond to shocks. In 30 other villages, savers choose their monitors. Proximate and central monitors are chosen.
Information flows. 63% of monitors tell others about the saver's progress. 15 months later, others know more about the saver's progress and believe she is responsible if the saver was assigned a more central monitor.

Laura Doval (Northwestern).

Matthew Elliott (Caltech and Cambridge). "Endogenous Financial Networks: Efficient Modularity and Why Shareholders Prevent It" with Jonathon Hazell

We consider systemic risk in financial networks, by examining the conflict of interest between debt- and equity-holders. Through trading, banks can diversify their idiosyncratic risks and avoid failures following small shocks. However, the resulting interdependencies can cause multiple failures after large shocks. A social planner resolves this trade-off by creating a modular network structure with fire breaks, thereby preventing failures from small shocks while containing contagion. Socially efficient networks favor debt-holders over equity holders, meaning equity-holders can profitably trade away from these networks. Moreover, profitable trades for equity holders align their counter-parties' failures with their own, creating systemic risk.

Maryam Farboodi (Princeton). "Meeting Technologies in Decentralized Asset Markets" with Gregor Jarosch and Robert Shimer

We study decentralized trading networks where agents differ in both their time-varying taste for an asset and the constant frequency at which they meet others. We demonstrate that fast agents endogenously arise as intermediators whose net valuation of the asset gets moderated through their exposure to others. We show that allocating meetings in an ex-ante asymmetric fashion across agents generates higher welfare then a homogeneous distribution of meeting frequencies, only if some agents intermediate. We also characterize properties of the market equilibrium in which ex-ante identical agents choose their meeting rates, and show that an equilibrium with symmetric meeting rates does not exist. Finally we compare the properties of equilibrium outcome with the planner allocation.

David Hirshleifer (UC Irvine). "Susceptibility in Networks: It's a Matter of Degree" with Ben Golub

Individuals' susceptibility to social influence varies with their social network positions. We show that the nature of this relationship is critical to how population traits evolve in the aggregate, and who has a large weight in determining that evolution. When per-neighbor susceptibility is increasing (decreasing) in degree, low (high) degree nodes have excess weight in moving the population average. We show this in a social network model that generalizes a standard model of trait updating to allow susceptibility to depend upon own degree, and show how this affects both the short- and long-run population trait evolution. We apply the model to the transmission of sociability and promiscuity. The model provides insight into why the friendship paradox and majority illusion do not necessarily result in disproportionate influence of the popular or highly connected.

Ben Golub (Harvard).

Matthew Jackson (Stanford). "The Friendship Paradox and Systematic Biases in Perceptions and Social Norms"

The ``friendship paradox'' (Feld 1991) is the structural implication of networks that, on average, peoples' friends have strictly more friends than the average person in a network. In particular, the number of people who observe a given person is proportional to the number of connections that the person has. This can distort perceptions of norms and behavior if more popular people in a society behave differently from less popular people. As I show here there are two things that drive people with more friends to behave differently from people with fewer friends. The first is that in any setting with strategic complementarities, people with more friends are exposed to greater interaction and influence. The second is that people who benefit more from a given activity will tend to form more relationships as they benefit more from the complementarities. These two effects lead people with more friends to choose more extreme actions, which in turn feeds back via the the friendship paradox to increase overall perceptions of behavior and then via complementarities to amplify average behavior. These theoretical results are consistent with the multitude of studies finding that students (from middle school through university) consistently overestimate peer consumption of alcohol, cigarettes, and drugs. This in turn amplifies students' own behaviors, and can help explain problems with adolescent abuse of drugs and alcohol, as well as other behaviors. I also discuss how these results change in cases of strategic substitutes, where individuals overestimate free-riding by peers.

Markus Mobius (MSR).

Pooya Molavi (MIT). "Foundations of non-Bayesian Social Learning" with Alireza Tahbaz-Salehi and Ali Jadbabaie

In this paper, we study the problem of boundedly rational learning in networks by taking an axiomatic approach. As our main behavioral assumption, we postulate that agents follow social learning rules that satisfy imperfect recall, according to which they treat the current beliefs of their neighbors as sufficient statistics for all the information available to them. We establish that as long as imperfect recall represents the only point of departure from Bayesian rationality, agents' social learning rules take a log-linear form. Our axiomatic approach enables us to provide a taxonomy of behavioral assumptions that underpin various boundedly rational models of learning, including the canonical model of DeGroot. We then provide a complete characterization of the asymptotic outcomes of social learning in our environment. We introduce the notion of group nonpolarization and show that it represents the necessary and sufficient condition for information aggregation. Our results on the asymptotic outcomes of social learning extend to a fairly large class of learning rules that admits as special cases both the log-linear and DeGroot Models. Finally, we show how the dispersion of information among different individuals in the social network determines the rate of learning.

Manuel Mueller-Frank (IESE). "A General Model of Boundedly Rational Observational Learning: Theory and Evidence" with Claudia Neri

This paper introduces a model of boundedly rational observational learning, which is rationally founded and applicable to general environments. Under Quasi-Bayesian updating each action is treated as if it were based only on the private information of its respective observed agent. We establish the long run learning properties of Quasi-Bayesian updating in a model of repeated interaction in social networks. Evidence from a laboratory experiment supports Quasi-Bayesian updating and our theoretical predictions.

Stephen Morris (Princeton). "Expectations, Networks, and Conventions" with Ben Golub

Mallesh Pai (Rice University). "Evaluating Forecasters" with Rahul Deb and Maher Said

Evan Sadler (Harvard). "Diffusion Games"

I study how the structure of a social network affects the diffusion of a new product or technology. A population of players interacts in a random network. Initially, few are aware of the opportunity to adopt a product, but a player becomes aware if a neighbor adopts. Payoffs may depend on neighbors' choices. To make this analysis tractable, I extend recent mathematical work on random graphs. A branching process approximates the local structure of the network, allowing us to characterize conditions under which diffusion results in a large cascade, the size of the cascade, and the rate of spread. This characterization facilitates comparative statics results for changes in the degree distribution, information about neighbors' valuations, clustering, and homophily. I apply the framework to study Bertrand competition with word-of-mouth communication and referral marketing.

Vasilis Syrgkanis (Microsoft Research). "Bayesian Exploration" with Yishay Mansour, Aleksandrs Slivkins and Zhiwei Steven Wu

We consider a ubiquitous scenario in the Internet economy when individual decision-makers (henceforth, agents) both produce and consume information as they make strategic choices in an uncertain environment. This creates a three-way tradeoff between exploration (trying out insufficiently explored alternatives to help others in the future), exploitation (making optimal decisions given the information discovered by other agents), and incentives of the agents (who are myopically interested in exploitation, while preferring the others to explore). We posit a principal who controls the flow of information from agents that came before to the ones that arrive later, and strives to coordinate the agents towards a socially optimal balance between exploration and exploitation, not using any monetary transfers. The goal is to design a recommendation policy for the principal which respects agents' incentives and minimizes a suitable notion of regret. We extend prior work in this direction to allow the agents to interact with one another in a shared environment: at each time step, multiple agents arrive to play a Bayesian game, receive recommendations, choose their actions, receive their payoffs, and then leave the game forever. The agents now face two sources of uncertainty: the actions of the other agents and the parameters of the uncertain game environment.

Lones Smith (Wisconsin). "Rushes in Large Timing Games" with Axel Anderson and Andreas Park

We develop a continuum player timing game that subsumes standard wars of attrition and pre-emption games, and introduces a new rushes phenomenon. Payoffs are continuous and single-peaked functions of the stopping time and stopping quantile. We show that if payoffs are hump-shaped in the quantile, then a sudden "rush" of players stops in any Nash or subgame perfect equilibrium. Fear relaxes the first mover advantage in pre-emption games, asking that the least quantile beat the average; greed relaxes the last mover advantage in wars of attrition, asking just that the last quantile payoff exceed the average. With greed, play is inefficiently late: an accelerating war of attrition starting at optimal time, followed by a rush. With fear, play is inefficiently early: a slowing pre-emption game, ending at the optimal time, preceded by a rush. The theory predicts the length, duration, and intensity of stopping, and the size and timing of rushes, and offers insights for many common timing games.

Philipp Strack (Berkeley). "The Speed of Social Learning" with Matan Harel, Elchanan Mossel and Omer Tamuz

We consider Bayesian agents who learn from exogenously provided private signals, as well as the actions of others. Our main finding is that increased interaction between the agents can lower the speed of learning: when two agents observe each other, learning is significantly slower than it is when one only observes the other. This slowdown is driven by a process in which a consensus on the wrong action causes the agents to discount new contrary evidence. We also show that very large groups of agents do not learn very quickly.

Adam Szeidl (Central European University). "Interfirm Relationships and Business Performance" with Jing Cai

We organize regular business meetings for randomly selected managers of young Chinese firms to study the effect of business networks on firm performance. We randomize 2,800 managers into several groups that hold monthly meetings for one year, and a ``no-meetings'' control group. We survey all firms before, after, and one year after the intervention. We find large, wide-ranging and persistent positive effects of business meetings. (1) The meetings increase firm revenue by 7.7 percentage points, and also increase profit, a management score, employment, and the number of business partners. (2) These effects persist one year after the intervention. We then exploit additional randomizations to identify mechanisms. (3) Firms randomized into groups with larger peer firms exhibit higher growth. (4) The meetings help diffuse randomly distributed business-relevant information, but diffusion is lower when group members are competitors. (5) Managers create more business partnerships with, and exhibit higher trust towards, those they meet every month than those they meet at one-time cross group meetings. We discuss policy implications for business associations in developing countries.

Ali Tahbaz-Salehi (Columbia). " "Information Aggregation in Heterogeneous Markets" with Yaarit Even and Xavier Vives

Omer Tamuz (Caltech). "Anonymous Network Games" with Wade Hann-Caruthers

We study a class of network games in which players are anonymous, in the sense that the game looks the same from each player's point of view. Fixing the local interaction, we compare the equilibria obtained on large finite networks and on infinite networks, and show that despite the fact that the game is locally indistinguishable to the agents, on infinite networks there is generally a richer equilibrium set.

Leeat Yariv (Caltech). "Optimal Dynamic Matching" with Mariagiovanna Baccara and SangMok Lee

We study a dynamic matching environment where individuals arrive sequentially. There is a tradeoff between waiting for a thicker market, allowing for higher quality matches, and minimizing agents' waiting costs. The optimal mechanism cumulates a stock of incongruent pairs up to a threshold and matches all others in an assortative fashion instantaneously. In decentralized settings, a similar protocol ensues in equilibrium, but expected queues are inefficiently long. We quantify the welfare gain from centralization, which can be substantial, even for low waiting costs. We also evaluate welfare improvements generated by transfer schemes and by matching individuals in fixed time intervals