Издательство InTech, 2010, -148 pp.Cognitive maps have emerged as an important tool in modeling and decision making. In a nutshell they are signed di-graphs that capture the cause/effect relationships that subject matter experts believe exist in a problem space under consideration. Each node in the map represents some variable concept. These generally fall into one of several hard categories: physical attributes of the environment, characteristics of artifacts embedded in the problem space, or one of several soft areas: decisions being made, social, psychological or cultural characteristics of the decision makers, intentions, etc. Part of the value of cognitive maps is that these hard and soft concepts can be seamlessly mixed in them to build a more robust model of the problem.
Edges in the map connect nodes for which a causal relationship is believed to exist. The edge is directed from the causal node to the effect node. In a general cognitive map, the edges have integer strengths of 1, indicating direct causality, -1, indicating inverse causality, and 0, indicating no causal link. A special type of cognitive maps, a fuzzy cognitive map, allows fuzziness in the modeling of the edge strengths. Unlike nodes that have crisp values, edge strengths can have any fractional value on the interval [-1,1], with fractional values indicating partial causality. Thus, relationships such as A somewhat affects B, or A really causes B can be captured and incorporated in the map. The ability to model partial causality in the map gives this technique great value in problem spaces that have complex interactions between the physical environment, man-made machines and decisions by human operators.
The map is a true model in the sense that it has predictive capabilities. In a typical situation, a set of nodes with known values are designated inputs. These values are applied to the map and held constant at their known values. In much the same way that voltage or current sources are sources of energy in an electrical circuit, these input nodes represent sources of causality in the map. These input values are then propagated through the map, using a user defined thresholding function at each node to map its inputs to one of the permissible nodal values. The process is repeated multiple times for all nodes in the map until one of two meta-situations develops. Either the map will reach equilibrium in the sense that the nodal values remain constant, or it will reach a limit cycle, an oscillatory condition where a group of nodes change back and forth between two more sets of values.
Unlike a state machine, the actual sequence that the causal values work through the map are generally unimportant. The map itself represents a systems-level model. As such the one must view all nodal values in the map as contributing to an understanding of its behavior. Thus, system behavior is indicated by the totality of nodal values present after the input values have been applied and propagated through it to equilibrium.
Cognitive maps have several distinct advantages over other decision making tools like decision trees or Petri nets. First, because states of nodes are compared to states of nodes, a common numeric metric is not necessary for all values. Instead changes in the underlying concepts of nodes are compared to changes to the underlying concepts in other nodes. Thus, cognitive maps truly allow apples to be compared to oranges. Second, feedback is allowed in the map. With feedback, effects can be mitigated (negative feedback) or reinforced (positive feedback) by certain causes. Finally, the map can be pieced together from many smaller maps through common nodes. Subject matter experts can then provide a model within their domain of expertise, without the need to understand to any great detail the relevant concepts from other areas of interest. Each subject matter expert can then focus on what they know best contributing to the development of a robust systems model. Interestingly, feedback and feedforward loops often appear when the individual maps are pieced together that are not present individually. This may explain why unseen problems can develop in a system even though each subsystem is thoroughly tested and validated.
Completed cognitive maps are be used in two general ways: decision assessment and system diagnosis. In decision assessment a set of known values are applied to input nodes in the map and allowed to propagate to equilibrium with an eye to the nodal values that result. The goal with this process is to assess what changes in the state of the system can be expected given a set of initial conditions. Part of the value of this technique is that the input nodes are not fixed. They can change depending on the context and the available information.
In system diagnosis, one is more interested in what inputs give rise to a system state of interest. The map affords ways to make this determination. Since they can be become very complex quickly as nodes are added, the topology of the model itself can be used as a diagnostic tool. Using basic matrix techniques nodes and combinations of nodes can be identified that are linked to the output nodes of interest through some, possibly lengthy, chain of cause-effect relationships in the map. By identifying sets of these causal nodes, input values can then be applied to them to see if the system state under examination results.
The chapters in this book cover a spectrum of insights into the development, use and applications of cognitive mapping and fuzzy cognitive mapping techniques.Topic Maps as Indexing Tools in the Educational Sphere: Theoretical Foundations, Review of Empirical Research and Future Challenges
A Cognitive Approach for Performance Measurement in Flexible Manufacturing Systems using Cognitive Maps
System Diagnosis Using Fuzzy Cognitive Maps
Subject-formal Methods Based on Cognitive Maps and the Problem of Risk Due to the Human Factor
From Physical Brain to Social Brain
The Role of Public Visual Art in Urban Space Recognition
The Representation of Objects in the Brain, and Its Link with Semantic Memory and Language: a Conceptual Theory with the Support of a Neurocomputational Model
Genetics of Cognition-What can Developmental Disorders Teach Us?
