Social behavior bias and knowledge management optimization

Yaniv Altshuler, Alex Sandy Pentland, Goren Gordon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Individuals can manage and process novel information only to some degree. Hence, when performing a perceptual novel task there is a balance between too little information (i.e. not getting enough to finish the task), and too much information (i.e. a processing constraint). Combining these new findings to a formal mathematical description of efficiency of novel information processing results in an inverted U-shape, wherein too little information is not effective to solving a problem, yet too much information is also detrimental as it requires more processing power than available. However, in an information flooded economic environment, it has been shown that humans are rather poor at managing information overload, which results in far from optimal performance. In this work we speculate that this is due to the fact that they are actually trying to maximize the wrong thing, e.g. maximizing monetary gains, while completely disregarding information management principles that underlie their decision-making. Thus, in a social decision-making environment, when information flows from one individual to another, people may “misuse” the abundance of information they receive. Using the model of individual novelty management, and the empirical statistical nature of investors’ inclination to information, we have derived the social network information flow dynamics and have shown that the “spread” of people’s position along the inverted U-shape of efficient information management leads to an unstable and inefficient macroscale dynamics of the network’s performance. This was in turn validated through a global inverted U-shape, observed in the macro-scale network performance.

Original languageEnglish
Title of host publicationSocial Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings
EditorsKevin Xu, Nitin Agarwal, Nathaniel Osgood
PublisherSpringer Verlag
Pages258-263
Number of pages6
ISBN (Electronic)9783319162676
DOIs
StatePublished - 2015
Externally publishedYes
Event8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015 - Washington, United States
Duration: 31 Mar 20153 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9021
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015
Country/TerritoryUnited States
CityWashington
Period31/03/153/04/15

Keywords

  • Behavioral modeling
  • Information flow
  • Information management
  • Social networks
  • Social physics

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