Beliefs in Decision-Making Cascades

This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an $N$-agent binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on preceding agents' decisions. In addition, the agents have their own beliefs instead of the true prior, and have nonidentical noise variances in the private signal. We focus on the Bayes risk of the last agent, where preceding agents are selfish. We first derive the optimal decision rule by recursive belief update and conclude, counterintuitively, that beliefs deviating from the true prior could be optimal in this setting. The effect of nonidentical noise levels in the two-agent case is also considered and analytical properties of the optimal belief curves are given. Next, we consider a predecessor selection problem wherein the subsequent agent of a certain belief chooses a predecessor from a set of candidates with varying beliefs. We characterize the decision region for choosing such a predecessor and argue that a subsequent agent with beliefs varying from the true prior often ends up selecting a suboptimal predecessor, indicating the need for a social planner. Lastly, we discuss an augmented intelligence design problem that uses a model of human behavior from cumulative prospect theory and investigate its near-optimality and suboptimality.Comment: final version, to appear in IEEE Transactions on Signal Processin

Disaster Mythology and Availability Cascades

Sociological research conducted in the aftermath of natural disasters has uncovered a number of “disaster myths” – widely shared misconceptions about typical post-disaster human behavior. This paper discusses the possibility that perpetuation of disaster mythology reflects an “availability cascade,” defined in prior scholarship as a “self-reinforcing process of collective belief formation by which an expressed perception triggers a chain reaction that gives the perception increasing plausibility through its rising availability in public discourse.” (Kuran and Sunstein 1999). Framing the spread of disaster mythology as an availability cascade suggests that certain tools may be useful in halting the myths’ continued perpetuation. These tools include changing the legal and social incentives of so-called “availability entrepreneurs” – those principally responsible for beginning and perpetuating the cascade, as well as insulating decision-makers from political pressures generated by the availability cascade. This paper evaluates the potential effectiveness of these and other solutions for countering disaster mythology. Las investigaciones sociológicas realizadas tras los desastres naturales han hecho evidentes una serie de “mitos del desastre”, conceptos erróneos ampliamente compartidos sobre el comportamiento humano típico tras un desastre. Este artículo analiza la posibilidad de que la perpetuación de los mitos del desastre refleje una “cascada de disponibilidad”, definida en estudios anteriores como un “proceso de auto-refuerzo de la formación de una creencia colectiva, a través del que una percepción expresada produce una reacción en cadena que hace que la percepción sea cada vez más verosímil, a través de una mayor presencia en el discurso público” (Kuran y Sunstein 1999). Enmarcar la propagación de los mitos del desastre como una cascada de disponibilidad sugiere que ciertas herramientas pueden ser útiles para parar la continua perpetuación de los mitos. Estas herramientas incluyen el cambio de los incentivos legales y sociales de los llamados “emprendedores de la disponibilidad”, los principales responsables del inicio y la perpetuación de la cascada, además del aislamiento de quienes toman las decisiones de las presiones políticas generadas por la cascada de disponibilidad. Este artículo evalúa la efectividad potencial de estas y otras soluciones para contrarrestar los mitos del desastre

Can You Believe Your Neighbors' Behaviors?

In the theoretical assumption of informational cascades, private signals and predecessors' actions are equivalently informative before informational cascades, but are not once informational cascades have started. This experimental study tests this assumption by measuring the informativeness of private signals and predecessors'' actions for human subjects in and out of informational cascades. We observed that subjects in informational cascades do not extract much information from predecessors'' actions, indicating that they recognize other subjects'' cascading behaviors, that subjects rely more on their private signals than on predecessors'' actions even when both of them are equivalently informative, and that subjects cannot estimate posterior beliefs precisely in a Bayesian way due to cognitive biases such as anchoring and adjustment or conservatism.

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information