Social Influences in Sequential Decision Making

Abstract

People often make decisions in a social environment. The present work examines social influence on people's decisions in a sequential decision-making situation. In the first experimental study, we implemented an information cascade paradigm, illustrating that people infer information from decisions of others and use this information to make their own decisions. We followed a cognitive modeling approach to elicit the weight people give to social as compared to private individual information. The proposed social influence model shows that participants overweight their own private information relative to social information, contrary to the normative Bayesian account. In our second study, we embedded the abstract decision problem of Study 1 in a medical decision-making problem. We examined whether in a medical situation people also take others' authority into account in addition to the information that their decisions convey. The social influence model illustrates that people weight social information differentially according to the authority of other decision makers. The influence of authority was strongest when an authority's decision contrasted with private information. Both studies illustrate how the social environment provides sources of information that people integrate differently for their decisions.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Weighting of public and private information. Marginal posterior distributions for the bias weight, the weight of the public information, and the weight of the private information.

The 95% highest density interval (HDI) spans 95% of the posterior distribution.

Fig 2. Empirically observed versus predicted probability judgments for Study 1 (A) and Study 2 (B). The general pattern of probability judgments (blue) is accurately captured by the predictions of the model solely derived from participants’ choices (green) for Study 1 (A) and Study 2 (B).

Probability judgments on the dashed line are in accordance with the Bayesian solution.

Fig 3. Weighting of different public and private information. Marginal posterior distributions for the bias weight, the weight of the public information derived from the higher ranked physician’s decisions, the weight of public information derived from equally ranked physicians’ decisions, and the weight of the private information.

The 95% highest density interval (HDI) spans 95% of the posterior distribution.