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Softsatisficing: Risk-sensitive softmax action selection.

Takumi Kamiya1, Tatsuji Takahashi1

  • 1Graduate School of Advanced Science and Engineering, Tokyo Denki University, Ishizaka, Hatoyama-machi, Hiki-gun, Saitama 350-0394, Japan.

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Summary
This summary is machine-generated.

Agents adjust exploration in complex environments based on aspiration levels, a behavior called satisficing. The new Softsatisficing model offers a risk-sensitive approach for decision-making in humans and animals.

Keywords:
Bounded rationalityExplorationMulti-armed bandit problemsOptimism in the face of uncertainty

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Area of Science:

  • Decision-making and Reinforcement Learning
  • Computational Neuroscience
  • Behavioral Economics

Background:

  • Agents often exhibit bounded rationality, adjusting exploration in complex environments rather than seeking optimal actions.
  • This adaptive exploration is linked to an internal aspiration level, a concept termed satisficing.
  • The Risk-sensitive Satisficing (RS) model quantifies this in reinforcement learning by evaluating actions relative to an aspiration level.

Purpose of the Study:

  • To analyze the Risk-sensitive Satisficing (RS) algorithm in comparison to UCB and Thompson sampling.
  • To investigate the differential risk-attitudes exhibited by the RS model.
  • To introduce and analyze the Softsatisficing policy as a stochastic extension of RS for modeling exploratory behavior.

Main Methods:

  • Comparative analysis of RS with established algorithms like UCB and Thompson sampling.
  • Development and theoretical analysis of the Softsatisficing policy, a stochastic equivalent of RS.
  • Examination of risk-sensitive exploratory behaviors implemented by both RS and Softsatisficing.

Main Results:

  • RS demonstrates effective risk-sensitive action value evaluation in reinforcement learning.
  • RS exhibits distinct risk-attitudes based on the specific risks encountered.
  • The proposed Softsatisficing policy provides a stochastic framework for risk-sensitive decision-making.

Conclusions:

  • The Softsatisficing policy is a valuable stochastic extension of the RS model.
  • Softsatisficing has significant potential for modeling risk-sensitive behaviors such as foraging in biological and organizational systems.
  • This work advances the understanding of bounded rationality and adaptive exploration in complex decision-making scenarios.