Cognitive Maps

К оглавлению
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 
34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 
51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 
68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 
85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 
102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 
119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 
136 137 138 139 140 141 142 143 144 145 146 

Hierarchies of super- and sub-classes for nodes and arcs are based on several observations

about their use in cognitive mapping. One observation is that object data structures

and methods of nodes tend to be quite similar in cognitive maps, but those of arcs

typically differ in important ways from each other. These differences require that class

structures representing the various arcs must differ accordingly. For example, the arcs

used in causal maps require a value to signify whether the relationship between the head

and tail nodes is directly or inversely proportional (e.g., “+” and “-”). Arcs used in

categorical maps represent similarities in linguistic meaning, so no variable exhibiting “+”

or “-” values is required. These differences between causal and categorical arcs require

differences in their data structures and methods. Class CausalArc would require a data

element for the proportionality variable and a constructor method incorporating a

proportionality variable as input. Class CategoricalArc would not have these requirements.

As with cognitive maps, the use of a super-class would offer the advantages of

abstraction: common data elements (e.g., variables referencing the nodes at the ends of

the arc) and methods would be embedded in a super-class (e.g., Class Arc). In turn,

Classes CausalArc and CategoricalArc would inherit those common characteristics.

Conclusion

Causal maps are useful but limited representations of human causal reasoning. Those

limitations constrain the usefulness of collections of causal maps in understanding and

promoting social causal cognition. The chapter addresses those limitations in several

ways.

First, an approach based on supplementing causal maps with other types of cognitive

maps was described. These cognitive maps represent many of the processes in social

causal reasoning that are not represented by causal maps, such as overcoming nuances

of human language and thought, surfacing and challenging the participants’ assumptions,

and documenting evidence from internal and external information repositories that

supports or contradicts causal assertions. Simply linking individual causal maps together

does not adequately represent these processes or their effects. Collections of

causal, associative, categorical, and other types of cognitive maps can better represent

the social processes of social causal reasoning than can causal maps alone.

The second lesson is that the information system representing the complex problem

space of social causal mapping must itself be sufficiently complex. A logical model for

a graph-theoretic, object-oriented social causal mapping system that is compatible with

the directed graph structure of causal maps was presented. Class models were described

that facilitate the incorporation of multiple, distributed types of organizational knowlObject-

edge maps into complementary, coherent representations. The use of classes and

encapsulated iterative traversal functions were shown to provide more complete, more

complex, derivable representations of knowledge at the organizational level. These

enriched representations can assist in identifying interesting patterns of variables and

relationships within the organization. These traversals were shown to assist in the

inventorying of organizational knowledge, as well as assist in addressing such traditional

organizational learning issues as equivocality reduction. These traversals can be

recorded, permitting the construction of molecular components for organizational

knowledge structure representation. Traditional relational databases that rely upon set

theory are not sufficiently powerful to represent social causal mapping (e.g., multiple

node-arc-node traversals).

The proposed social causal mapping system can be used to support an organization’s

learning and knowledge management capabilities by facilitating the efficient and effective

representation, construction, and integration of molecular components of organizational

knowledge for the identification of interesting organization-level knowledge

structures. These knowledge structures can then be used to identify contributing

individuals and records that can provide the “fluid mix of framed experience, values,

contextual information, and expert insight that provides a framework for evaluating and

incorporating new experiences and information” (Davenport & Prusak, 1998). The

proposed system may have other uses as well. For example, it could be used as a human

resources tool to measure the extent to which employees contribute to learning and

knowledge across the organization. This objective could be achieved by monitoring who

contributes to the social causal mapping system, how often those contributions are used

by others, and to what extent the contributions play a part in strategic, tactical, or

operational improvements, product or service innovations, or increased organizational

competitiveness.

Future work is needed before the proposed approach can be fully implemented. Classes

of heuristics for the identification and manipulation of interesting components of

organizational memory remain to be identified. Suitable applications of graph-theoretic

algorithms remain to be tested. Object and class models for organizational knowledge

structure representation must still be implemented.

References

Axelrod, R. (1976). Structure of decision: The cognitive maps of political elites.

Princeton, NJ: Princeton University Press.

Boland, R. J., Tenkasi, R.V., & Te’eni, D. (1994). Designing information technology to

support distributed cognition. Organization Science, 5, 456-475.

Booch, G. (1994). Object-oriented analysis and design with applications. 2nd edition.

Reading, MA: Addison-Wesley.

Bougon, M. G. (1992). Congregate cognitive maps: A unified dynamic theory of organization

and strategy. Journal of Management Studies, 29, 369-389.

Codd, E. F. (1970). A relational model of data for large shared data banks. Communications

of the ACM, 13, 377-387.

Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage

what they know. Boston: Harvard Business School Press.

Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische

Mathematik, 1, 269-271.

Eden, C. S., Ackermann, F., & Cropper, C. (1992). The analysis of cause maps. Journal

of Management Studies, 29, 309-324.

Fiol, C. M., & Huff, A.S. (1992). Maps for managers. Journal of Management Studies, 29,

267-285.

Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: MIT Press.

Huber, G. P. (1991). Organizational learning: The contributing processes and the literatures.

Organization Science, 2, 88-115.

Huff, A. S. (1990). Mapping strategic thought. In A. S. Huff (Ed.), Mapping strategic

thought (pp. 11-49). Chichester: John Wiley & Sons.

Langfield-Smith, K. (1992). Exploring the need for a shared cognitive map. Journal of

Management Studies, 29, 349-368.

Laukkanen, M. (1994). Comparative cause mapping of organizational cognitions. Organization

Science, 5, 322-343.

Lyles, M.A. (1985). Organizational learning. Academy of Management Review, 10, 803-

813.

Montazemi, A. R., and Conrath, D. W. (1986). The use of cognitive mapping for

information requirements. Management Information Systems Quarterly, 19(1), 44-

57.

Morecroft, J. D. W. (1988). System dynamics and microworlds for policymakers. European

Journal of Operational Research, 35, 301-320.

Narayanan, V. K., & Fahey, L. (1990). Evolution of revealed causal maps during decline:

A case study of Admiral. In A. S. Huff (Ed.), Mapping strategic thought (pp.109-

134). Chichester: John Wiley & Sons.

Nelson, K. M., Nadkarni, S., Narayanan, V. K., and Ghods, M. (2000). Understanding

software operations support expertise: A causal mapping approach. Management

Information Systems Quarterly, 24, 475-507.

Senge, P. M. (1990). The fifth discipline. New York: Doubleday.

Shaw, M. (1984, October). Abstraction techniques in modern programming languages.

IEEE Software, 10-26.

Smith, D.C.P. (1977b). Database abstractions: Aggregation and generalization. ACM

Transactions on Database Systems, 2, 105-133.

Smith, J. M., & Smith, D.C.P. (1977a). Database abstractions: Aggregation. Communications

of the ACM, 20, 405-413.

Stein, E. W., & Zwass, V. (1995). Actualizing organizational memory with information

systems. Information Systems Research, 6, 85-117.

Sterman, J.D. (1992). Systems thinking and organizational learning: Acting locally and

thinking globally in the organization of the future. European Journal of Operational

Research, 59, 137-150.

Ullman, J. D. (1988). Principles of database and knowledge-base systems. Rockville, MD:

Computer Science Press.

Weick, K. E. (1979). The social psychology of organizing, 2nd edition. Reading, MA:

Addison-Wesley.

Winston, P. H. (1984). Artificial intelligence. Reading, MA: Addison-Wesley.