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![Page 1: Cognitive Maps and Bayesian Networks Emel Aktaş. Outline Cognitive Maps Influence Diagrams Bayesian Networks.](https://reader035.fdocuments.us/reader035/viewer/2022062422/56649ed35503460f94be3b8b/html5/thumbnails/1.jpg)
Cognitive Maps and Bayesian Networks
Emel Aktaş
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Outline
Cognitive Maps
Influence Diagrams
Bayesian Networks
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Motivation for Graphical Models
Systematic construction methods
Efficient inference procedures
Explicit encoding of independencies
Modular representation of probabilities
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Taxonomy of Network Based Representation Schemes
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Introduction
Person’s thinking about a problem or issue Tolman (1948)
Cognition
Various fields: Psychology Planning Geography Management
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Some Definitions
Cause-effect networks Srinivas and Shekar (1997)
Graphical descriptions Axelrod (1976); Eden (1990)
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Usage
Knowledge / belief representation
Capturing causality
Network-based formalisms Cognitive Maps (Axelrod, 1976) Belief Networks (Pearl, 1988) Qualitative Probabilistic Networks (Wellman, 1990)
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Causal Maps
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Causal map
Network representation of beliefs Nodes and arcs Directed graph
Harary et al. (1965); Harary (1972)
Ideas and actions Visible thinking
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Mapping
Documentary coding
Interviews Subjective world of the interviewee
Questionnaire survey Groups
Personal construct theory Kelly (1955)
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Representation
Short pieces of text
Unidirectional arrows
cause
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Merging Decision of a College
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Heads and tails
Incoming or outgoing arrows Negative relationships minus sign
Signed directed graph
Head: no outgoing arrows; goals / outcomes
Tail: no incoming arrows; options Centrality
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Structural properties
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Example(Ulengin, Topcu, Onsel, 2001)
Contribution to social
improvement Damage on the inhabitants of the surrounding area
by crossing
Damage to the historical texture of the
region
Cost of nationalization
Possibility of increasing
employment through creating jobs
-
Construction time
Facility in constructing (the topographic structure of
the crossing area and the surrounding land, etc)Suitability for
urban, regional and national
progress plans
-
Suitability for the transportation
policy
-
Financial damage in case of accidents during operation
headcentral
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Problem/issue complexity
Cognitive scientists /organizational scientists
Central features total number of nodes total number of arrows cognitive centrality of particular nodes
Ratio of arrows to concepts 1.15 to 1.20 for maps elicited from interviews
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The extent of the map
More nodes more complex
Mutual understanding of the issue
Number of concepts
length of the interview
skills of the interviewer
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Representation
Graph See the causal relationships better
Matrix Mathematical analysis
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Example: How can we motivate employees?
Variables Motivation Salaries Problems in the work environment Good attitude of the employer Good attitude of the colleagues Carreer possibilities
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positive (+)
salary + motivation
Causal relationships between the variables
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positive (+)
salary + motivation negative (-)
Problems in the work environment - motivation
Causal relationships between the variables
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positive (+)
salary + motivation negative (-)
Problems in the work environment - motivation
No relationship (0)
attitude of colleagues 0 salary
Causal relationships between the variables
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Determination of the causal relationships
Square matrix including all concepts Pairwise comparisons
mtv. sal. env. emp. col. car.
mtv. 0 0 0 0 0 0
sal. + 0 0 0 0 0
env. - 0 0 0 0 0
emp. + 0 - 0 0 +
col. + 0 0 0 0 0
car. + 0 0 0 0 0
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How can we motivate employees?
mtv.
sal.
env.
+emp. +
-
col.
+
car.+
+
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Causal Map of a Cement Producer
25
Economic setback
Investments on infrastructure,
residential and non-residential buildings
Development of
construction industry
Environmental concerns
Pressure on environmental
issues
Application of Kyoto Protocol
Competition
Regulations
Demand – supply balanceInput
costs
Capacity usage
Profitability
Cement demand
Restructuring of big players
Consolidation and vertical integration
+
+
+
+
+
+
-+
+-
-
-+
+
+
++
-
-+
+
Decreasing energy supply
+
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Islands of themes
without accounting for hierarchy
Nodes in each cluster tightly linked
Bridges with other clusters minimized
Cluster Analysis
26
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Hierarchical Clusters
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Potent Options
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Construction of the Group Cognitive Map
Gather related concepts from different persons Prepare a collective list of concepts Persons’ pairwise comparisons Construct of personal cognitive maps Aggregate personal cognitive maps
Single number of experts Taking experts’ opinions again about the doubtful relations
Size Over 100 nodes on the map
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30
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The most fundamental decisions are Definition of customer service (1) Forecasts of demand (8) Product routing (14) Information to be provided with the product (32)
The rest of the decisions cannot be taken unless these 4 decisions are given
Hierarchy of decisions
31
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Definition of customer service
32
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Centrality
33
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First Cluster
34
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Second Cluster
35
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Influence Diagrams
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Compact graphical /mathematical representation of a decision situation
It is a generalization of a Bayesian network, probabilistic inference problems
decision making problems
Influence Diagrams
37
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Nodes Decision node [rectangle] Uncertainty node [oval] Deterministic node [double oval] Value node [diamond]
Arcs Functional arcs (ending in value node) Conditional arcs (ending in uncertainty /
deterministic node) Informational arcs (ending in decision node)
Influence Diagrams
38
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ID of a Plan for Vacation
http://en.wikipedia.org/wiki/Influence_diagram
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Decisions about the Marketing Budget and Product Price
http://www.lumina.com/software/influencediagrams.html
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Bayesian Networks
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Bayesian Networks
Probabilistic graphical model Variables
Probabilistic dependencies
Uncertain, ambiguous, and/or incomplete domains
Directed acyclic graphs
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Bayesian Network Structure
A Bayesian Network has 3 components: X, S and P; X= {X1; X2;…; Xn} variablesS: causal structureP: conditional probabilities
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Bayesian Network Structure
Rearrangement of the causal map Acyclic (no loops allowed) Direct and indirect relationships
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Bayesian Network Structure
F is dependent on C and D A and B: root nodes F and G: leaves P(A,B,C,D,E,F,G)=P(G/D) P(F/C,D) P(E/B) P(D/A,B) P(C/A) P(A) P(B)
A B
C
F
D E
G
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Bayesian Network Steps
1. Specification of the variables
2. Specification of the network structure
3. Determination of the conditional probabilities
4. Acquisition of additional knowledge
5. Inference based on knowledge
6. Interpretation of the results
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Example: Product Development
4 variables Market Dynamics Product Life Cycle Market Leadership Rate of Product Launch
Dependence relations should be defined as conditional probabilities.
Nadkarni and Shenoy (2001)
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Example: Product Development
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Bayesian Network Structure
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Bayesian Network Structure
P(D,C,L,R)=P(D)*P(C/D)*P(L)*P(R/C,L) Two variables are conditionally independent if
there is no arrow relating them No arrow between D and L L is
independent on D.
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Knowledge Inference
Bayesian network is constructed Conditional probabilities are defined. It is possible to infer knowledge now using
specific software. Hugin (www.hugin.com) Netica (www.norsys.com)
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Knowledge Inference
market dynamics
highlow
75.025.0
product life cycle
shortlong
73.726.3
market leadership
leaderfollower
90.010.0
rate of product launch
highlow
77.122.9
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Knowledge Inference
market dynamics
highlow
0 100
product life cycle
shortlong
10.090.0
market leadership
leaderfollower
90.010.0
rate of product launch
highlow
52.048.1
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A cognitive map – bayesian network application
Domain: tomography section within the radiology department of a private hospital in Turkey,
Objective: improve management system performance
The hospital operates 42 branches, including clinical research, diagnostics, and outpatient and inpatient care, with 279 expert physicians and 1038 healthcare and support staff.
A total of 36,000 radiological tests are conducted per annum.
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Framework
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Variables of the System
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Preliminary Causal Map
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Revised Causal Map
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Discretization of the Variables
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Final Causal Map
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Compiled Bayesian Network
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Additional Knowledge: Type of Scrutiny Known
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The case where type of scrutiny and medicine treatment is known
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Target values for the parent variables of time spent for scrutiny
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Sensitivity of ‘‘time spent for scrutiny’’ based on findings at another node
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References Axelrod, R., 1976. Structure of Decision. University of Princeton Press,
Princeton. Eden, C., 1988. Cognitive mapping: A review. European Journal of Operational
Research 36, 1-13. Harary, F., Norman, R., Cartwright, D., 1965. Structural Models: An Introduction
to the Theory of Directed Graphs. Wiley, New York. Harary, F., 1972. Graph Theory. Addison-Wesley, Reading. Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference. Morgan Kaufmann, San Mateo, CA. Nadkarni S., Shenoy, P., 2001. A Bayesian network approach to making
inferences in causal maps, European Journal of Operational Research 128(3),479-498.
Srinivas V., Shekar B., 1997. Applications of uncertainty-based mental models in organizational learning: A case study in the Indian automobile industry. Accounting, Management and Information Technologies, 7(2), 87-112.
Tolman E. C., 1948. Cognitive Maps in Rats and Man. Psychological Review 55: 189-208.
Wellman M.P., 1990. Fundamental Concepts of Qualitative Probabilistic Networks. Artificial Intelligence, 44(3):257–303.