The Effects of Amount of Information Provided and Feedback of Results on Decision Making Efficiency

1965 ◽  
Vol 7 (6) ◽  
pp. 513-520 ◽  
Author(s):  
Charles H. Hammer ◽  
Seymour Ringel
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Decision Accuracy ◽  
Independent Variables ◽  
Amount Of Information ◽  
Taking Action

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.

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

2020 ◽  
Author(s):  
Vincenz Frey ◽  
Arnout van de Rijt
Keyword(s):  
Decision Making ◽  
Social Information ◽  
Experimental Studies ◽  
Decision Makers ◽  
Sequential Decision Making ◽  
The Internet ◽  
Central Tendency ◽  
Sequential Decision ◽  
Group Members ◽  
Positive Effect

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.

scholarly journals Optimal Policies for Quantum Markov Decision Processes

2021 ◽  
Author(s):  
Ming-Sheng Ying ◽  
Yuan Feng ◽  
Sheng-Gang Ying
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Quantum Systems ◽  
Sequential Decision Making ◽  
Mathematical Framework ◽  
Sequential Decision ◽  
Learning Techniques ◽  
Optimal Policies ◽  
Markov Decision ◽  
Programming Algorithms

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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