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Social Exchange Theory

Social Exchange Theory

We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).

Social Exchange Theory

Social Exchange Theory

As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...

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Related Experiment Video

Updated: Feb 11, 2026

Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions

Collection and Long-Term Maintenance of Leaf-Cutting Ants Atta in Laboratory Conditions

Published on: August 30, 2022

Ant Lion Optimization algorithm for kidney exchanges.

Eslam Hamouda¹, Sara El-Metwally¹, Mayada Tarek¹

¹Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Dakahlia, Egypt.

|May 4, 2018

Summary

This summary is machine-generated.

Kidney exchange programs facilitate transplants for incompatible pairs. A novel Ant Lion Optimization algorithm maximizes exchanges, outperforming other methods in efficiency and results.

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Area of Science:

Transplantation science
Operations research
Bio-inspired computing

Background:

Kidney exchange programs (KEPs) enable transplants for incompatible patient-donor pairs.
KEPs involve complex optimization problems considering patient outcomes and resource limitations.

Purpose of the Study:

To introduce a bio-inspired stochastic algorithm, Ant Lion Optimization (ALO), for kidney exchange.
To maximize feasible cycles and chains in kidney exchange pools.

Main Methods:

Applied the Ant Lion Optimization (ALO) algorithm to model kidney exchange.
Compared ALO with deterministic (integer programming) and other stochastic (Genetic Algorithm) methods.

Main Results:

ALO achieved comparable results to deterministic approaches.
ALO outperformed Genetic Algorithms in computational efficiency and exchange quantity.
ALO demonstrated adaptability for online exchanges and incorporating patient weights.

Conclusions:

The Ant Lion Optimization algorithm is a viable and efficient tool for kidney exchange optimization.
ALO offers advantages in computational resource usage and maximizing transplants.
The algorithm can be readily adapted for advanced KEP decision-making.