Testing Models of Social Learning on Networks: Evidence From Two Experiments

Abstract

We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types-Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model;(2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.A. G. CHANDRASEKHAR, H. LARREGUY, AND J. P. XANDRI information from their network neighbors when engaging in learning. This constraint may be for many reasons, including but not limited to operating in settings where learning is through observations of others' actions or the costs of communicating very complex information are too high and therefore only summaries are transmitted. 2 We study such a coarse communication environment. We focus on a setting in which individuals receive signals about an unknown binary state of the world in the first period and, in subsequent rounds, they communicate to their neighbors their best (binary) guess about the state of the world. 3 In this ubiquitous setting, if all agents are Bayesian, and there is common knowledge of this, under mild assumptions, learning will be asymptotically efficient in large networks (see Gale and Kariv (2003) and Mossel and Tamuz (2010) for a myopic learning environment and Mossel, Sly, and Tamuz (2015) for a strategic learning environment). However, if all agents update their guess as the majority of their neighbors' prior guesses-as modeled by a coarse DeGroot model of learning, also known as the majority voting model (Liggett (1985))-then it is possible that a non-trivial set of agents will end up stuck making the wrong guess. In practice, it might be that there is a mix of sophisticated (Bayesian) and naive (DeGroot) learners, and that Bayesians are aware of this and incorporate it in their calculations. Such an incomplete information model, its relevance, and implications for asymptotic learning have not been studied in our coarse communication environment.This paper develops an incomplete information model of social learning with coarse communication on a network in which agents can potentially be Bayesian or DeGroot, and agents have common knowledge of the distribution of Bayesian or DeGroot types in the population. Bayesian agents then learn in an environment of incomplete information. The model nests the two extreme cases-complete information all-Ba...

Network structure and naive sequential learning

“…A growing body of recent evidence in psychology and economics shows that people learning from peers are often not fully correct in their treatment of social structure (Chandrasekhar et al 2020, Enke and Zimmermann 2017). Instead of calculating the optimal Bayesian behavior that fully takes into account all they know about the network and signal structure, agents often apply heuristic simplifications to their environment.…”

“…To answer this question, our model must capture key behavioral patterns in how individuals process social information in these learning settings. Empirical research on social learning suggests that humans often exhibit inferential naiveté , failing to understand that their predecessors' actions reflect a combination of private information and the inference those predecessors have drawn from the behavior of still others (e.g., Chandrasekhar et al 2020, Eyster et al 2015). Returning to the examples, a consumer may mistake a herd on a product for evidence that everyone has positive private information about the product's quality.…”

Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks

“…Consider the system in (11), with individuals' relative interactions captured by the strongly connected . Suppose that the initial conditions satis- 3 and . Then,…”

“…for every and , and where is individual 's susceptibility to influence as detailed below (3). In fact, the first reflected selfappraisal model proposed by Noah Friedkin in [65] considered (18) rather than (6).…”

“…Eventually, a consensus is reached on the opinion value, i.e., there is agreement across the opinions of all individuals, if the network satisfies some connectedness conditions. Experimental validations of the DeGroot model are reported in [3,4]. A quite different approach to the DeGroot model, not analysed in this paper, but which has also provided great insight is the use of kinetic modelling for describing opinion dynamics [5][6][7][8] .…”