Networks are natural tools for understanding social and economic phenomena. For example, all markets are characterized by agents connected by complex, multilateral information networks, and the network structure influences economic outcomes. In an earlier study, we undertook an experimental investigation of learning in various three-person networks, each of which gives rise to its own learning patterns. In the laboratory, learning in networks is challenging and the difficulty of solving the decision problem is sometimes massive even in the case of three persons. We found that the theory can account surprisingly well for the behavior observed in the laboratory. The aim of the present paper is to investigate important and interesting questions about individual and group behavior, including comparisons across networks and information treatments. We find that in order to explain subjects’ behavior, it is necessary to take into account the details of the network architecture as well as the information structure. We also

We study the (perfect Bayesian) equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). We characterize pure-strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning—convergence (in probability) to the right action as the social network becomes large. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of “expansion in observations”. We also characterize conditions under which there will be asymptotic learning when private beliefs are bounded.

Nearly all observational learning models assume that individuals can observe all the decisions that have previously been made. In reality, such perfect information is rarely available. To explore the difference between observational learning under perfect and imperfect information, this paper takes an experimental look at a situation in which individuals learn by observing the behavior of their immediate predecessors. Our experimental design uses the procedures of Çelen and Kariv (2004a) and is based on the theory of Çelen and Kariv (2004b). We find that imitation is much less frequent when subjects have imperfect information, even less frequent than the theory predicts. Further, while we find strong evidence that under perfect information a form of generalized Bayesian behavior adequately explains behavior in the laboratory, under imperfect information behavior is not consistent even with this generalization of Bayesian behavior.

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We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. An underlying state determines payoffs from different actions. Agents decide which others to form a costly communication link with incurring the associated cost. After receiving a private signal correlated with the underlying state, they exchange information over the induced communication network until taking an (irreversible) action. We define asymptotic learning as the fraction of agents taking the correct action converging to one in probability as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the induced communication network most agents are a short distance away from “information hubs”, which receive and distribute a large amount of information. Asymptotic learning therefore requires information to be aggregated in the hands of a few agents. We also show that while truthful communication may not always be a best response, it is an equilibrium when the communication network induces asymptotic learning. Moreover, we contrast equilibrium behavior with a socially optimal strategy profile, i.e., a profile that maximizes aggregate welfare. We show that when the network induces asymptotic learning, equilibrium behavior

We provide an overview of recent research on belief and opinion dynamics in social networks. We discuss both Bayesian and non-Bayesian models of social learning and focus on the implications of the form of learning (e.g., Bayesian vs. non-Bayesian), the sources of information (e.g., observation vs. communication), and the structure of social networks in which individuals are situated on three key questions: (1) whether social learning will lead to consensus, i.e., to agreement among individuals starting with different views; (2) whether social learning will effectively aggregate dispersed information and thus weed out incorrect beliefs; (3) whether media sources, prominent agents, politicians and the state will be able to manipulate beliefs and spread misinformation in a society.

When choosing between alternatives of uncertain quality, one can learn from other decision-makers either through communicating with them about the reasons underlying their choices, or through simply observing their choices. The former way of learning has been extensively studied in the word-of-mouth (WOM) literature, while the latter way, labelled silent word-of-mouth (SWOM), has received less attention. This paper is the ¯rst individual-level empirical study to structurally model how SWOM in°uences choices. I consider a setting where a group of individuals sequentially decide whether to adopt a product of unknown quality. Each individual is equipped with a private quality signal. Under WOM, one incorporates her predecessors ' signals, as well as her own signal, in her quality evaluation. Under SWOM, she observes her predecessors ' decisions, infers what their signals have been, and updates her quality evaluation. I show that WOM and SWOM can lead to distinct choice patterns in the marketplace in that decisions are more likely to be imitated under SWOM. The model is applied to a high-stake decision environment where patients on a transplant

we will study two topics that are the subject of much current research: (1) strategic transmission of private information; (2) social and economic networks. The goal is to introduce you to the basic tools and models in these fields and to facilitate your transition from coursework to your own research. Pre-requisites: Knowledge of non-cooperative game theory at the level of a first year Ph.D. course is sufficient. You should be comfortable with solving games of incomplete information and refinements of Nash equilibrium such as sequential equilibrium. Some background in general microeconomics is also helpful (the first year Ph.D. micro sequence is more than enough).

This paper studies a simple model of observational learning where agents care not only about the information of others but also about their actions. We show that despite the complex strategic considerations that arise from forward-looking incentives, herd behavior can arise in equilibrium. Our results have application to understanding momentum in sequential elections, and inefficiencies in sequential public good contributions and leadership charitable giving. Keywords: Social learning, herd behavior, payoff interdependence,

We present a model of word-of-mouth interaction. Agents interact on fixed, regular social network and exchange information in order to make a choice between multiple alternatives. Communicated information includes rumors. The model also includes the inertia in economic behavior. We analyze the organization of behavior in the long run. We show that for a large range of initial conditions clustering in economic behavior in case of two-option environment emerges and persists indefinitely. Multi-option setup is also investigated. Here it turns out that long-run option popularity distribution is heavily skewed with few options dominating large portion of social space.