Name
Abstract | ||
|---|---|---|
We identify the of a human social group as the communication network(s) within that group. We then introduce three theoretical approaches to analyzing what determines the structures of human organizations. All three approaches adopt a group-selection perspective, so that the groupu0027s network structure is (approximately) optimal, given the information-processing limitations of agents within the social group, and the exogenous welfare function of the overall group. In the first approach we use a new sub-field of telecommunications theory called network coding, and focus on a welfare function that involves the ability of the organization to convey information among the agents. In the second approach we focus on a scenario where agents within the organization must allocate their future communication resources when the state of the future environment is uncertain. We show how this formulation can be solved with a linear program. In the third approach, we introduce an information synthesis problem in which agents within an organization receive information from various sources and must decide how to transform such information and transmit the results to other agents in the organization. We propose leveraging the computational power of neural networks to solve such problems. These three approaches formalize and synthesize work in fields including anthropology, archeology, economics and psychology that deal with organization structure, theory of the firm, span of control and cognitive limits on communication. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Social and Information Networks | Social group,Theory of the firm,Linear network coding,Telecommunications network,Organizational structure,Social organization,Computer science,Artificial intelligence,Artificial neural network,Management science,Machine learning,Span of control |
DocType | Volume | Citations |
Journal | abs/1702.04449 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David H. Wolpert | 1 | 4334 | 591.07 |
| Justin Grana | 2 | 0 | 0.68 |
| Brendan Tracey | 3 | 64 | 6.94 |
| Tim Kohler | 4 | 1 | 1.15 |
| Artemy Kolchinsky | 5 | 71 | 8.85 |