Posts in Category: simulation

computational simulation

Paper: Social influence and collective opinion formation

The rule is perfect: in all matters of opinion our adversaries are insane.
Mark Twain, Christian Science (1907, Book 1, Ch. 5)




Mehdi Moussaïd, Juliane E. Kämmer, Pantelis P. Analytis, Hansjörg Neth

Social influence and the collective dynamics of opinion formation

Abstract:  Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. 

Paper: Melioration as rational choice

Maximization (…) is not a general explanatory principle for behavior. (…)
Melioration (…) is the dynamic process controlling allocation of time across response alternatives.
Herrnstein & Vaughan (1980). Melioration and behavioral allocation, p. 143+172

Chris R. Sims, Hansjörg Neth, Robert A. JacobsWayne D. Gray

Melioration as rational choice: Sequential decision making in uncertain environments

Abstract:  Melioration — defined as choosing a lesser, local gain over a greater longer term gain — is a behavioral tendency that people and pigeons share.  As such, the empirical occurrence of meliorating behavior has frequently been interpreted as evidence that the mechanisms of human choice violate the norms of economic rationality.  In some environments, the relationship between actions and outcomes is known. In this case, the rationality of choice behavior can be evaluated in terms of how successfully it maximizes utility given knowledge of the environmental contingencies.  In most complex environments, however, the relationship between actions and future outcomes is uncertain and must be learned from experience.  When the difficulty of this learning challenge is taken into account, it is not evident that melioration represents suboptimal choice behavior. 

Paper: Competitive mate choice

Hansjörg Neth, Simeon Schächtele, Sulav Duwal, Peter M. Todd

Competitive mate choice: How need for speed beats quests for quality and harmony

Abstract:  The choice of a mate is made complicated by the need to search for partners at the same time others are searching. What decision strategies will outcompete others in a population of searchers? We extend previous approaches using computer simulations to study mate search strategies by allowing direct competition between multiple strategies, evaluating success on multiple criteria. In a mixed social environment of searchers of different types, simple strategies can exploit more demanding strategies in unexpected ways. We find that simple strategies that only aim for speed can beat more selective strategies that aim to maximize the quality or harmony of mated pairs.

Paper: Feedback design for controlling a dynamic multitasking system

If an organism is confronted with the problem of behaving approximately rationally,
or adaptively, in a particular environment, the kinds of simplifications that are suitable
may depend not only on the characteristics—sensory, neural, and other—of the organism,
but equally on the nature of the environment.
H.A. Simon (1956), Rational choice and the structure of the environment, p. 130

[Copyright, 2008]:

Hans Neth, Sunny Khemlani, Wayne Gray (2008)

Feedback design for the control of a dynamic multitasking system: Dissociating outcome feedback from control feedback. Human Factors Journal, 2008.

Hansjörg Neth, Sangeet S. Khemlani, Wayne D. Gray

Feedback design for the control of a dynamic multitasking system: Dissociating outcome feedback from control feedback

Objective: We distinguish outcome feedback from control feedback to show that suboptimal performance in a dynamic multitasking system may be caused by limits inherent to the information provided rather than human resource limits.