scholarly journals High Stakes

2017 ◽  
Author(s):  
Iain R. Brennan
Keyword(s):  
Decision Making ◽  
Research Evidence ◽  
Rational Analysis ◽  
High Stakes ◽  
High Reward ◽  
Complex Decision Making ◽  
Potential Benefits ◽  
Complex Decision ◽  
Victim Resistance ◽  
Benefits And Costs

This chapter describes the contradictory roles that weapons play in offender decision making as mechanisms that can both increase the physical harm to a victim of violence and also reduce the need for physical harm in victims of robbery. Because weapons serve simultaneously offensive and defensive purposes, the way in which offenders carry and use weapons is subject to a complex decision-making process. This process is presented and interpreted from a rational perspective, incorporating an offender’s calculation of potential benefits and costs as well as the uncertainty of a victim’s response. A rational analysis of weapon carrying and use is presented along with research evidence suggesting that culture and availability are important influences on weapon of choice and weapon-related behavior. The chapter concludes with a review of the effectiveness of weapons in reducing victim resistance and retaliation showing that weapon use is a high-reward/high-cost activity.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

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.

The effect of the cognitive demands of the distraction task on unconscious thought

2014 ◽  
Vol 37 (1) ◽  
pp. 44-45 ◽  
Author(s):  
Laurent Waroquier ◽  
Marlène Abadie ◽  
Olivier Klein ◽  
Axel Cleeremans
Keyword(s):  
Decision Making ◽  
Unconscious Thought ◽  
Cognitive Demands ◽  
The Unconscious ◽  
Unconscious Processes ◽  
Distraction Task ◽  
Complex Decision Making ◽  
Complex Decision

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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