Decision Making as a Socio-Cognitive Process

Author(s):  
Monique Borges ◽  
João Lourenço Marques ◽  
Eduardo Anselmo Castro

Researchers from multidisciplinary scientific fields have been puzzled by human behaviour in dynamic and complex decision-making contexts. Since the seventeenth century, several theoretical, conceptual, and empirical contributions have emerged. These contributions evidence the need to critically assess the rational foundations of decision theories, stemming from the cognitive basis for human heuristics and bias. This chapter focuses on how socio-cognitive theories have been introduced as analytic tools to explain individual and collective behaviours, decision rules, and cognitive mechanisms. In particular, the authors advance some arguments explaining its importance and the underlying challenges of social representations as part of the decision-making process. They propose a methodological script that stresses the social representations approach and encounters more functional and operational settings.

Author(s):  
Monique Borges ◽  
João Lourenço Marques ◽  
Eduardo Anselmo Castro

Researchers from multidisciplinary scientific fields have been puzzled by human behaviour in dynamic and complex decision-making contexts. Since the seventeenth century, several theoretical, conceptual, and empirical contributions have emerged. These contributions evidence the need to critically assess the rational foundations of decision theories, stemming from the cognitive basis for human heuristics and bias. This chapter focuses on how socio-cognitive theories have been introduced as analytic tools to explain individual and collective behaviours, decision rules, and cognitive mechanisms. In particular, the authors advance some arguments explaining its importance and the underlying challenges of social representations as part of the decision-making process. They propose a methodological script that stresses the social representations approach and encounters more functional and operational settings.


2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.


2014 ◽  
Vol 37 (1) ◽  
pp. 44-45 ◽  
Author(s):  
Laurent Waroquier ◽  
Marlène Abadie ◽  
Olivier Klein ◽  
Axel Cleeremans

AbstractThe unconscious-thought effect occurs when distraction improves complex decision making. Recent studies suggest that this effect is more likely to occur with low- than high-demanding distraction tasks. We discuss implications of these findings for Newell & Shanks' (N&S's) claim that evidence is lacking for the intervention of unconscious processes in complex decision making.


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