Effect of perceived controllability and performance standards on self-regulation of complex decision making.

1989 ◽  
Vol 56 (5) ◽  
pp. 805-814 ◽  
Author(s):  
Albert Bandura ◽  
Robert Wood
Keyword(s):  
Decision Making ◽  
Self Regulation ◽  
Performance Standards ◽  
Complex Decision Making ◽  
Complex Decision ◽  
And Performance

Age Differences in Complex Decision Making and Judgment

2021 ◽  
pp. 203-233
Author(s):  
Klara Rydzewska ◽  
Maciej Koscielniak ◽  
Bettina von Helversen ◽  
Grzegorz Sedek
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Individual Differences ◽  
Age Differences ◽  
Motivational Factors ◽  
Age Related ◽  
Complex Decision Making ◽  
Complex Decision ◽  
And Performance

This chapter discusses age differences in complex decision making and judgment, particularly the role of motivational factors and individual differences. Literature on the influence of age-related changes in cognition and motivation on search and performance in complex decision making is reviewed. The role of financial incentives, need for cognition, and need for cognitive closure is discussed, including the age-related influence of motivational factors on the performance of sequential decision-making tasks. Additionally, the role of feedback as a factor producing superior performance of older adults in a decision-making task is introduced. Moreover, novel research findings regarding connections between intellectual helplessness and information and communication technologies in older adults are presented. Lastly, individual differences in numeracy and intellectual helplessness in mathematics as predictors of age-related differences in performance of multiattribute tasks are described.

Influence of Analytically and Intuitively Framed Instructions upon Multi-Attribute Decision Task Approach

2002 ◽  
Vol 46 (3) ◽  
pp. 497-500 ◽  
Author(s):  
Rebecca J. White ◽  
Thomas E. Nygren
Keyword(s):  
Decision Making ◽  
Task Performance ◽  
Decision Task ◽  
Task Instructions ◽  
Decision Making Styles ◽  
Complex Decision Making ◽  
Multi Attribute Decision Making ◽  
Complex Decision ◽  
And Performance

Individuals may rely upon a number of decision making strategies in their approach to a complex decision making environment. For example, people may have a predisposition to rely upon intuitive and analytical decision making styles during task performance. These decision making styles, as measured by a Decision Making Styles Inventory (DMI), have been found to predict performance on a multi-attribute decision making task. It follows that manipulating the manner in which task instructions are framed, either analytically or intuitively, may have an influence upon task approach and performance as well. Influence of analytic and intuitive instructions for a multi-attribute decision making task are examined in this paper.

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.

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