scholarly journals Subjective optimality in finite sequential decision-making

2021 ◽  
Vol 17 (12) ◽  
pp. e1009633
Author(s):  
Yeonju Sin ◽  
HeeYoung Seon ◽  
Yun Kyoung Shin ◽  
Oh-Sang Kwon ◽  
Dongil Chung
Keyword(s):  
Decision Making ◽  
Time Window ◽  
Programming Model ◽  
Secretary Problem ◽  
Optimal Decision ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Model Based ◽  
Subjective Valuation ◽  
Optimal Decision Making

Many decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals’ suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals’ deviations from optimality and predicts the choice behaviors under fewer and more opportunities. We further identified that pupil dilation reflected the levels of decision difficulty and subsequent choices to accept or reject the stimulus at each opportunity. The value sensitivity, a model-based estimate that characterizes each individual’s subjective valuation, correlated with the extent to which individuals’ physiological responses tracked stimuli information. Our results provide model-based and physiological evidence for subjective valuation in finite sequential decision-making, rediscovering human suboptimality in subjectively optimal decision-making processes.

scholarly journals Subjective optimality in finite sequential decision-making

2020 ◽  
Author(s):  
Yeonju Shin ◽  
HeeYoung Seon ◽  
Yun Kyoung Shin ◽  
Oh-Sang Kwon ◽  
Dongil Chung
Keyword(s):  
Decision Making ◽  
Time Window ◽  
Programming Model ◽  
Secretary Problem ◽  
Optimal Decision ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Model Based ◽  
Subjective Valuation ◽  
Optimal Decision Making

AbstractMany decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals’ suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals’ deviations from optimality and predicts the choice behaviors under fewer opportunities. We further identified that pupil dilation reflected the levels of decision difficulty and subsequent choices to accept or reject the stimulus at each opportunity. The value sensitivity, a model-based estimate that characterizes each individual’s subjective valuation, correlated with the extent to which individuals’ physiological responses tracked stimuli information. Our results provide model-based and physiological evidence for subjective valuation in finite sequential decision-making, rediscovering human suboptimality in subjectively optimal decision-making processes.

scholarly journals Opportunity Costs in the Game of Best Choice

10.37236/8322 ◽  
2019 ◽  
Vol 26 (1) ◽  
Author(s):  
Madeline Crews ◽  
Brant Jones ◽  
Kaitlyn Myers ◽  
Laura Taalman ◽  
Michael Urbanski ◽  
...  
Keyword(s):  
Decision Making ◽  
Classical Case ◽  
Secretary Problem ◽  
Sequential Decision Making ◽  
Opportunity Costs ◽  
Sequential Decision ◽  
Probability Of Success ◽  
Uniform Model ◽  
Weighted Model ◽  
Over Time

The game of best choice, also known as the secretary problem, is a model for sequential decision making with many variations in the literature. Notably, the classical setup assumes that the sequence of candidate rankings is uniformly distributed over time and that there is no expense associated with the candidate interviews. Here, we weight each ranking permutation according to the position of the best candidate in order to model costs incurred from conducting interviews with candidates that are ultimately not hired. We compare our weighted model with the classical (uniform) model via a limiting process. It turns out that imposing even infinitesimal costs on the interviews results in a probability of success that is about 28%, as opposed to 1/e (about 37%) in the classical case.

scholarly journals Model-based decision-making and its metacontrol in childhood

2020 ◽  
Author(s):  
Claire Rosalie Smid ◽  
Wouter Kool ◽  
Tobias U. Hauser ◽  
Nikolaus Steinbeis
Keyword(s):  
Decision Making ◽  
Computational Cost ◽  
Cost Benefit ◽  
Optimal Decision ◽  
Developmental Studies ◽  
Free System ◽  
Model Based ◽  
Model Free ◽  
Human Decision ◽  
Optimal Decision Making

Human decision-making is underpinned by distinct systems that differ in their flexibility and associated computational cost. A widely accepted dichotomy distinguishes a flexible but costly model-based system and a cheap but rigid model-free system. Optimal decision-making requires adaptive arbitration between these two systems depending on environmental demands. Previous developmental studies suggest that model-based decision-making only emerges in adolescence. Here, we show that when using a paradigm more conducive to model-based decision-making, children as young as 5 years show contributions from a model-based system to their behaviour. Furthermore, we find that between the ages 5 to 11, children demonstrate increasing metacontrol, which is the engagement of cost-benefit arbitration over decision-making systems on a trial-by-trial basis. Our results suggest that model-based decision-making emerges much earlier than previously believed, while adaptive arbitration between computationally cheap and costly systems continues to undergo developmental changes during childhood.

scholarly journals Hunger improves reinforcement-driven but not planned action

2021 ◽  
Author(s):  
Maaike M.H. van Swieten ◽  
Rafal Bogacz ◽  
Sanjay G. Manohar
Keyword(s):  
Decision Making ◽  
Learning Systems ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Context Sensitive ◽  
Decision Systems ◽  
Model Based ◽  
Model Free ◽  
Model Free Control ◽  
Overall Performance

AbstractHuman decisions can be reflexive or planned, being governed respectively by model-free and model-based learning systems. These two systems might differ in their responsiveness to our needs. Hunger drives us to specifically seek food rewards, but here we ask whether it might have more general effects on these two decision systems. On one hand, the model-based system is often considered flexible and context-sensitive, and might therefore be modulated by metabolic needs. On the other hand, the model-free system’s primitive reinforcement mechanisms may have closer ties to biological drives. Here, we tested participants on a well-established two-stage sequential decision-making task that dissociates the contribution of model-based and model-free control. Hunger enhanced overall performance by increasing model-free control, without affecting model-based control. These results demonstrate a generalized effect of hunger on decision-making that enhances reliance on primitive reinforcement learning, which in some situations translates into adaptive benefits.

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