Cognitive Maps
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.