scholarly journals Social Influence Undermines the Wisdom of the Crowd in Sequential Decision Making

Author(s):  
Vincenz Frey ◽  
Arnout van de Rijt

Teams, juries, electorates, and committees must often select from various alternative courses of action what they judge to be the best option. The phenomenon that the central tendency of many independent estimates is often quite accurate—“the wisdom of the crowd”—suggests that group decisions based on plurality voting can be surprisingly wise. Recent experimental studies demonstrate that the wisdom of the crowd is further enhanced if individuals have the opportunity to revise their votes in response to the independent votes of others. We argue that this positive effect of social information turns negative if group members do not first contribute an independent vote but instead cast their votes sequentially such that early mistakes can cascade across strings of decision makers. Results from a laboratory experiment confirm that when subjects sequentially state which of two answers they deem correct, majorities are more often wrong when subjects can see how often the two answers have been chosen by previous subjects than when they cannot. As predicted by our theoretical model, this happens even though subjects’ use of social information improves the accuracy of their individual votes. A second experiment conducted over the internet involving larger groups indicates that although early mistakes on easy tasks are eventually corrected in long enough choice sequences, for difficult tasks wrong majorities perpetuate themselves, showing no tendency to self-correct. This paper was accepted by Yuval Rottenstreich, decision analysis.

Author(s):  
Charles H. Hammer ◽  
Seymour Ringel

Sixty subjects worked a series of sequential decision making tasks in which the amount of information provided and feedback of results were the independent variables. Data were collected on decision accuracy, confidence in decision accuracy, and judged sufficiency of the information provided. Accuracy, confidence in accuracy, and ratings of sufficiency increased as amount of information provided was increased. Feedback produced increases in decision accuracy only. For forty percent of all correct responses, subjects judged the information provided to be insufficient as a basis for taking action. These data strongly suggest that lack of confidence in their ability to make accurate decisions may cause some decision makers to delay taking action even when they are able to make an accurate decision on the basis of the information available.


2021 ◽  
Vol 18 (179) ◽  
pp. 20210082
Author(s):  
Richard P. Mann

Social animals can improve their decisions by attending to those made by others. The benefit of this social information must be balanced against the costs of obtaining and processing it. Previous work has focused on rational agents that respond optimally to a sequence of prior decisions. However, full decision sequences are potentially costly to perceive and process. As such, animals may rely on simpler social information, which will affect the social behaviour they exhibit. Here, I derive the optimal policy for agents responding to simplified forms of social information. I show how the behaviour of agents attending to the aggregate number of previous choices differs from those attending to just the most recent prior decision, and I propose a hybrid strategy that provides a highly accurate approximation to the optimal policy with the full sequence. Finally, I analyse the evolutionary stability of each strategy, showing that the hybrid strategy dominates when cognitive costs are low but non-zero, while attending to the most recent decision is dominant when costs are high. These results show that agents can employ highly effective social decision-making rules without requiring unrealistic cognitive capacities, and indicate likely ecological variation in the social information different animals attend to.


2012 ◽  
Vol 279 (1735) ◽  
pp. 1977-1985 ◽  
Author(s):  
Frédérique Dubois ◽  
Luc-Alain Giraldeau ◽  
Denis Réale

Although natural selection should have favoured individuals capable of adjusting the weight they give to personal and social information according to circumstances, individuals generally differ consistently in their individual weighting of both types of information. Such individual differences are correlated with personality traits, suggesting that personality could directly affect individuals’ ability to collect personal or social information. Alternatively, the link between personality and information use could simply emerge as a by-product of the sequential decision-making process in a frequency-dependent context. Indeed, when the gains associated with behavioural options depend on the choices of others, an individual's sequence of arrival could constrain its choice of options leading to the emergence of correlated behaviours. Any factor such as personality that affects decision order could thus be correlated with information use. To test this new explanation, we developed an individual-based model that simulates a group of animals engaged in a game of sequential frequency-dependent decision: a producer–scrounger game. Our results confirm that the sequence of decision, in this case enforced by the order in which animals enter a foraging area, consistently influences their mean tactic use and their individual plasticity, an outcome reminiscent of the correlation reported between personality and social information use.


Author(s):  
Ming-Sheng Ying ◽  
Yuan Feng ◽  
Sheng-Gang Ying

AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.


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