Agent based approach to semi-spatial socioeconomic interactions
Fig. 1: Mobile application for studying location based sociological patterns (running on Motorola E1000). The 9 icons represent everyday-life aesthetic schemes to describe emotions associated with social goods (events, locations,...).

Several important models of socioeconomic interactions do not depend so much on spatial distance, they are rather governed by social distance. Over time, social distance (connectivity) has increased in importance for many forms of interaction while spatial distance (proximity) has decreased in importance. The growing role of social distance in socioeconomic interactions has followed advances in transportation and communications technologies and quite recently, this trend has accelerated explosively.
Spatial modeling. Several projects, such as 'Kaisersrot' [1] or [4] have focused purely on spatial aspects in urban modeling. This allows, for example, to optimize space usage for two groups of inhabitants, where group (i) wants to live close to the church and group (ii) wants to live away from the church.
Non-spatial modeling. In the present project we build the simulation on top of the space sociology put forward in [2] and [3]. Although it creates a lot of references to spatial problems, the primary focus is on:
1. social goods and humans,
2. relationships and associations,
3. atmospheres (in the sense of Heideggers existential philosophy).
Consequently the spatial model we are using is distorted by the above mentioned three non-spatial aspects. Hence we call it a semi-spatial model.

With the present model we develop a tool to carry out 'what-if' scenarios in the space sociological context, with both urban and architectural applications.

Field studies. For reliable simulations, a large body of space sociological data, is required. To obtain this information, a simple to use data harvesting application was developed in JAVA (see Fig. 1).

Defining Agents

Each individual is characterized by a list whose elements indicate the individual's attributes. These attributes are represented by numbers and lists of numbers. Attributes may include the individual's name, a list of the names of the members of the individual's social network and usually one or more of the following:
1. social status
2. wealtch
3. a list of the values of the individual's culturally determined memes (eg., religious or political affiliation),
4. a list of various other attributes (eg., gender, race, intelligence, risk preferences, etc.)

Defining Relationship Qualities

Relationship qualities are based on everyday-aesthetic schemes introduced by G. Schulze in [5]. The model is described in detail in the article Visualisation and Design in Space Sociology. For simulation models, these qualities can be distributed among agents based on statistical analyses, which are also available from [5].

Introducing Social Network Interaction

To introduce social interaction, we create a function, that is applied directly to the society list. This function applies an interaction rule to each individual and his social network. The argument of the interaction rule is a nested list whose elements are the attribute list of an individual and the attribute lists of the members of his social network.
Since the data harvesting tool is based on neural networks, it detects and modifies the rules over time. These rules are included in the simulation on a continuous basis.

Basic Examples of Social Network Interaction

For purposes of illustration, we introduce two elementary network interaction rules. These are the only rules introduced by the author. All other rules of the simulation are generated by the server through neural networks.
The first, emulate, models the effect of emulating those with higher status in one's social network. The second interaction rule, friendsOfFriends, provides an individual with a list of individuals who are acquaintances of members of his social network.

Emulate:: Each individual in this society is represented by a four-tuple attribute list of the form {name, socNetLis, socialStatus, memeLis} where the first attribute is the person's name, the second attribute is the list of the names of the members of her social network, the third attribute is the social status of the person, and the fourth attribute is his meme list.
During a time step, each individual locates the person in his social network who has the highest social status and changes the value of one of his memes, randomly selected, to equal the value of the corresponding meme of that person.

FriendsOfFriends:: This rules take a nested list containing the attribute lists of a person and the members of his social network (friends) and returns a four-tuple consisting of (1) the person's name, (2) an ordered pair whose first element is the list of the names of his friends and the second element is a list of lists of the names of his friends' friends, (3) his social status, and (4) his meme list.

Data Harvesting Application; Technical Aspects

To feed the simulations with reliable data it is necessary to gain a reasonable set of data from a representative number of human agents. Therefore, an easy to use JAVA application was developed for mobile phones.
Whenever the human agent experiences something important, he can use his mobile phone to (i) take a photo and (ii) describe his immediate associations with the event. The emotions are described via everyday-life-aesthetic schemes and the according visualization tool illustrated in Visualisation and Design in Space Sociology. The JAVA application sends these pieces of information together with the GPS coordinates and the current time back to a central server.

Pattern detection. The server is running a pattern detection software based on neural networks. This is an adaptive algorithm which tries to figure out patterns in the input data it obtains from its agents attached via IP. Over time, the algorithm develops rules with a certain probability which are included in the simulation. Hence, the more input comes from human agents, the more reliable the simulation becomes.


[1] K. Christaanse, Projekt “Kaisersrot”,
[2] Martina Löw, Raumsoziologie, Suhrkamp, 2001
[3] Martina Löw, Die Differenzierung des Städtischen, Leske & Budrich, 2002
[4] I. Benenson, S. Aronovich, S. Noam, Let’s talk objects: generic methodology for urban high-resolution simulation, Computers, Environment and Urban Systems, 2004.
[5] Gerhard Schulze, Die Erlebnisgesellschaft, Kultursoziologie der Gegenwart, Campus Fachbuch, 2000

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