A Mathematical Model of Group Social Interaction
Bibliographic Data
| ID | 4140580 |
|---|---|
| Authors | R Baker (0000-0002-3451-2018, UNSW Sydney, corresponding author), R G V Baker (UNSW Sydney) |
| Year | 1982 |
| Volume | 14 |
| Issue | 8 |
| Pages | 1031-1046 |
| Publication date | 1982-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning A Economy and Space (JOURNAL) |
| Journal identifiers | ISSN: 0308-518X • E-ISSN: 1472-3409 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1068/a141031 |
| OpenAlex | W1982333713 |
| Language | EN |
| Citations received | 1 |
| References cited | 13 |
The definition of place utility from potential theory shows that a preference and orthogonal indifference field can influence a decisionmaker, which suggests the operation of behavioural forces. Learning energy is defined to be a major behavioural force and a statistical model is proposed for its distribution over an assembly of decisionmakers. An attitude system is derived for a social network where the decisionmaker can be in a unitary state of utility or disutility. The resulting partition function shows that the learning energy of the assembly is derived from nearest-neighbour interactions and the operation of an external place utility preference field. A positive linear relationship is proposed between learning energy and environmental stress. The critical stress in social interaction is determined and a variety of behaviours in urban sociology are predicted. The model is then applied to a Markov learning system and the results discussed
Econometrics · Machine learning · Markov chain · Mathematical economics · Partition function (quantum field theory) · Physics · Political science · Preference · Pure mathematics · Social learning · Statistical physics · Statistics · Unitary state · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Psychology · Social Psychology · Theoretical and Computational Physics · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,08 |
| Citation span | 2014 - 2014 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |