Social Behavior Bias and Knowledge Management Optimization
© 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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2021
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| Online Access: | https://hdl.handle.net/1721.1/137812 |
| _version_ | 1826200585912713216 |
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| author | Altshuler, Yaniv Pentland, Alex Gordon, Goren |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Altshuler, Yaniv Pentland, Alex Gordon, Goren |
| author_sort | Altshuler, Yaniv |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T11:38:40Z |
| format | Article |
| id | mit-1721.1/137812 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:38:40Z |
| publishDate | 2021 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | mit-1721.1/1378122021-11-09T03:24:34Z Social Behavior Bias and Knowledge Management Optimization Altshuler, Yaniv Pentland, Alex Gordon, Goren Massachusetts Institute of Technology. Media Laboratory © 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. 2021-11-08T20:13:39Z 2021-11-08T20:13:39Z 2015 2019-07-26T16:18:18Z Article http://purl.org/eprint/type/ConferencePaper 0302-9743 1611-3349 https://hdl.handle.net/1721.1/137812 Altshuler, Yaniv, Pentland, Alex and Gordon, Goren. 2015. "Social Behavior Bias and Knowledge Management Optimization." en 10.1007/978-3-319-16268-3_27 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer Nature MIT web domain |
| spellingShingle | Altshuler, Yaniv Pentland, Alex Gordon, Goren Social Behavior Bias and Knowledge Management Optimization |
| title | Social Behavior Bias and Knowledge Management Optimization |
| title_full | Social Behavior Bias and Knowledge Management Optimization |
| title_fullStr | Social Behavior Bias and Knowledge Management Optimization |
| title_full_unstemmed | Social Behavior Bias and Knowledge Management Optimization |
| title_short | Social Behavior Bias and Knowledge Management Optimization |
| title_sort | social behavior bias and knowledge management optimization |
| url | https://hdl.handle.net/1721.1/137812 |
| work_keys_str_mv | AT altshuleryaniv socialbehaviorbiasandknowledgemanagementoptimization AT pentlandalex socialbehaviorbiasandknowledgemanagementoptimization AT gordongoren socialbehaviorbiasandknowledgemanagementoptimization |