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A computational model of selection by consequences.

¹Department of Psychology, Emory University, Atlanta, Georgia 30322, USA. psyjjmd@emory.edu

Journal of the Experimental Analysis of Behavior

|September 11, 2004

Summary

This study modeled Darwinian selection by consequences in a digital organism. The model consistently demonstrated a hyperbolic relationship between response and reinforcement rates, mirroring biological findings.

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Area of Science:

Computational neuroscience
Behavioral economics
Evolutionary computation

Background:

Darwinian selection by consequences is a fundamental principle in biology.
Understanding the computational underpinnings of behavior is crucial for advancing artificial intelligence and behavioral science.

Purpose of the Study:

To instantiate Darwinian selection by consequences in a computational model.
To investigate the behavior of a digital organism under reinforcement schedules.
To explore the relationship between response and reinforcement rates.

Main Methods:

Developed a computational model simulating a digital organism with a repertoire of behaviors.
Subjected the digital organism to selection, reproduction, and mutation over multiple generations.
Arranged reinforcement according to random-interval (RI) schedules in computational experiments.
Varied quantitative and qualitative features of the model.

Main Results:

The digital organism consistently exhibited a hyperbolic relationship between response and reinforcement rates.
The hyperbolic function provided a superior fit to the data compared to other function forms.
Model parameters systematically varied with quantitative and qualitative changes, aligning with biological organism findings.

Conclusions:

The material events driving behavior on RI schedules are computationally equivalent to Darwinian selection by consequences.
The developed computational model offers a promising dynamic account of behavior.
Further research into this computational model is warranted.

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