Social Behavior Bias and Knowledge Management Optimization
Name
SBP15-MiceAndTraders.pdf
Description
Accepted version
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211.82 KB
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Unknown
Checksum (MD5)
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Author(s)
Altshuler, Yaniv
Pentland, Alexander Sandy
Gordon, Goren
Date Issued
2015
Publisher
Springer Nature
Citation
Altshuler, Yaniv, Pentland, Alex and Gordon, Goren. 2015. "Social Behavior Bias and Knowledge Management Optimization."
Version
Author's final manuscript
Abstract
© Springer International Publishing Switzerland 2015. 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. We suggest that changing the distribution of people’s position along the information management axis can have drastic effects on the network performance. Two basic manipulations can be considered from a physical system analogy: (i) changing the “temperature” of the system, i.e. either raising it to create a more diverse spread or lowering it to make a more homogenous network; (ii) by lowering the system’s temperature one can then tune the distribution center to be more in the optimal efficient information management regime.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-16268-3_27
