I.        Introduction


By defining a „communityš as a „dynamic wholeš it is then possible to apply field dynamic theory and topology to the investigation of community-life. Kurt Lewin (1951) distinguished between „physical, psychological, and sociological wholesš (p. 305). Lewin follows the pioneering work of Kohler in Gestalt psychology where the dynamic whole is defined as „the dependence of its parts.š Thus, community can be defined as a collectivity of individuals affecting each other on a stable basis. Kurt Lewin‚s topological psychology, developed in the 1920‚s and 1930‚s in Germany, and continued in the 1940‚s and 1950‚s in the U.S., is an empirically worked out notation system suitable for graphing exactly the dependency relationships among the individuals forming community.


2.       The Criterion Issue


How much interdependence is required to transform a collection into a collectivity or a community? Clearly, individuals who share a traffic pathway in a field or around a common or shared facility, do form a collection in that every individual present has a direct relationship to the shared facility, and is affected by the action of other individuals (e.g. crowding). Is this a sufficient condition to constitute „dependence of its partsš? Rather than choosing some arbitrary cut-off point, is it feasible to develop empirical criteria in terms of functioning quality or characteristic. For example: are there consequences of degrees of dependence for overall measures of functioning?


With collectivities established by individual organisms, degree of depend–ence, or interdependence, is variable within observable limits. That is, individual A shows no signs of being affected by the behavior of B, up to a certain point, then past a particular limit, A is affected (e.g. when B approaches A from a distance).


As well, B‚s behavior may not visibly affect A while C (or some physical condition) is present; remove C, and suddenly A is affected by the same behavior of B that left A unaffected earlier. We may refer to both these cases as conditional dependence. Therefore we can specify that the phenomenon of community organization is a variable and conditional dynamic state characterizing the interdependence of a collection of individual organisms.


3.       The Gradient Approach


Lewin proposes a definition for interdependence as a sliding gradient from independence to dependence. Independence between two individuals A and B is defined as that condition which maximal changes in B‚s behavior affect A within specified minimal limits (e.g., when A shows no visible correlated change in its behavior during the time of observation).


Dependence between A and B is defined as the inverse of independence, so that the interdependence value of a dynamic whole may be designated by a proportion.






                                                                                                 B <  3, A = 0
                                                                                                 B =  5, A = 1
                                                                                                 B =  7, A = 2
                                                                                                 B =  8, A = 5






Figure 1:  Gradient showing proportion values defining functional inter-dependence between two variables.


The definitional implications, or the consequences of this definition, include: (i) asymmetry between A‚s dependence on B and B‚s dependence on A; (ii) clustering of areas of dependence and independence so that some behaviors may show dependence between A and B, while other behaviors may show independence between A and B.


A corollary of the above is that a dyadic relationship (e.g., a pair or couple) will exhibit 3 kinds of dependency relation:

(1)      Independence in some behavioral areas;

(2)      Asymmetrical dependence in some behavioral areas;

(3)      Interdependence (i.e. symmetrical dependence) in some behavioral areas.


4.      Relationship: The Unit of Dependence in Community Organization


Since the particular type of dyadic dependence varies in accordance to behavioral areas observed, it is necessary in each experiment to sample daily round behaviors in the normal day-to-day existence of the individuals being studied. This sampling, in some suitable and justified form, yields an empirical taxonomy of daily round community settings, a sort of spatiotemporal map specifying the actual behavioral incidents of an individual with particular other individuals. One might also think of this as a dynamic field graph in the sense that when the data it contains are mapped unto a n-dimensional graph, its inspection, description, and extrapolation yields new empirical hypotheses. Social psychologist Kurt Lewin and Founder of Pragmatism, Charles Peirs, among others, have developed extensive and workable notation systems for graphing the dynamic field properties of social situations. Skinner‚s „cumulative recordsš, Newcomb‚s „sociometric interactionsš, Bales‚ „interpersonal group spaceš, Osgood‚s „semantic spaceš, Ogden‚s „semantic oppositionš, Barker‚s „behavior settingš, the U.S. Government‚s GNP and Standard of Life Index, Roget‚s „classification schemeš, Freud‚s "psychic premuesš, conflicts and „blocksš, and a host of other commonly used terms, all provide us with field theory concepts that fit nicely unto the dynamic field-graph representation.


There are two possible approaches in taxonomic description when dealing with Ruman interactions: solitary or independent and standardized. Only the latter will be dealt with here. Standardized taxonomic descriptions are, as a rule, based on interpersonally verifiable criteria. This őtype of taxonomic representation or field graph is known as objective reporting or witnessing.


When the behavior to be reported can be specified in advance and reliably reported (two necessary premises), the data are objective but reduced („abstractedš). Abstract and reduced data are, as a rule, most informative when averaged or statistically transformed. On the other hand, when reduction is not desirable because of the level of concepts being studied, objective reporting or witnessing follows a less determinative format: new and unforeseen categories of obser–vation may emerge spontaneously and be part of the data. Thus, „the dataš

is extracted from the spontaneous, witness‚ records, and this extraction process turns up new categories not yet represented on the taxonomy.


When examining such witness‚ records of observed interactions between individuals, the problem of What is the unit? is constantly present. It is best to see it as analogous to a dynamic system that strives for balance when disturbed: whatever unit is chosen, other units are subsequently possible at different levels of integration (see: „systems theoryš).


Hierarchical unitizingš, as we might call this type of use of spontaneous data, allows the objective use of witness‚s reports. The same standardized objectivity and reliability is called for as in less complex, instrumented reports usable when behaviors can be pre-specified and recuded (or abstracted) for later statistical treatment.