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11
Bidirectional Reasoning in Decision Making by Constraint Satisfaction
 Journal of Experimental Psychology: General
, 1999
"... Recent constraintsatisfaction models of explanation, analogy, and decision making claim that these processes are influenced by bidirectional constraints that promote coherence. College students were asked to reach a verdict in a complex legal case involving multiple conflicting arguments, including ..."
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Cited by 57 (4 self)
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Recent constraintsatisfaction models of explanation, analogy, and decision making claim that these processes are influenced by bidirectional constraints that promote coherence. College students were asked to reach a verdict in a complex legal case involving multiple conflicting arguments, including alternative analogies to the target case. Participants rated agreement with the individual arguments both in isolation before seeing the case, and again after reaching a verdict. Assessments of the individual arguments (including the competing analogies) shifted so as to cohere with their emerging verdict. Information about the character of the defendant in the initial case triggered a cascade of "spreading coherence", influencing decisions made about a subsequent case involving very different legal issues. Participants ' memory for their initial positions also shifted so as to cohere with their final positions. The coherence shifts were simulated by a constraint satisfaction model. The results demonstrate that an alogical process of constraint satisfaction can transform highly ambiguous inputs into coherent decisions. Bidirectional Reasoning 3 One of the most deeprooted assumptions about human reasoning is that the flow of
College sophomores in the laboratory: Influences of a narrow data base on social psychology's view of human nature
 Journal of Personality and Social Psychology
, 1986
"... For the 2 decades prior to 1960, published research in social psychology was based on a wide variety of subjects and research sites. Content analyses show that since then such research has overwhelmingly been based on college students tested in academic laboratories on academiclike tasks. How might ..."
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Cited by 54 (0 self)
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For the 2 decades prior to 1960, published research in social psychology was based on a wide variety of subjects and research sites. Content analyses show that since then such research has overwhelmingly been based on college students tested in academic laboratories on academiclike tasks. How might this heavy dependence on one narrow data base have biased the main substantive conclusions of sociopsychological research in this era? Research on the full life span suggests that, compared with older adults, college students are likely to have lesscrystallized attitudes, lessformulated senses of self, stronger cognitive skills, stronger tendencies to comply with authority, and more unstable peer group relationships. The laboratory setting is likely to exaggerate all these differences. These peculiarities of social psychology's predominant data base may have contributed to central elements of its portrait of human nature. According to this view people (a) are quite compliant and their behavior is easily socially influenced, (b) readily change their attitudes and (c) behave inconsistently with them, and (d) do not rest their selfperceptions on introspection. The narrow data base may also contribute to this portrait of human nature's (e) strong emphasis on cognitive processes and to its lack of emphasis on (f) personality dispositions, (g) material selfinterest, (h) emotionally based irrationalities, (i) group norms, and (j) stagespecific phenomena. The analysis implies the need both
Frustration vs. Clusterability in TwoMode Signed Networks (Signed Bipartite Graphs)
, 2010
"... Abstract. Mrvar and Doreian recently defined a notion of bipartite clustering in bipartite signed graphs that gives a measure of imbalance of the signed graph, different from previous measures (the “frustration index ” or “line index of balance”, l, and Davis’s clusterability). A biclustering of a b ..."
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Cited by 3 (1 self)
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Abstract. Mrvar and Doreian recently defined a notion of bipartite clustering in bipartite signed graphs that gives a measure of imbalance of the signed graph, different from previous measures (the “frustration index ” or “line index of balance”, l, and Davis’s clusterability). A biclustering of a bipartite signed graph is a pair (π1, π2) of partitions of the two color classes; the sets of the partitions are called clusters. The majority biclusterability index M(k1, k2) is the minimum number of edges that are inconsistent, in a certain definition, with a biclustering, over all biclusterings with π1  = k1 and π2  = k2. Theorems: M(1, k2) ≥ l, while M(k1, k2) ≤ l if k1, k2 ≥ 2. For K2,n with n ≥ 2, M(2, 2) = l in about 1/3 of all signatures. If n> 2, then for every signature of K2,n there exists a biclustering with π1  = π2  = 2 such that M(π1, π2) = l. There are many open questions. Keywords. Twomode signed network, signed bipartite graph, frustration index, majority clusterability, bipartite clusterability, biclustering. Mathematics Subject Classifications (2010): Primary 05C22; Secondary 91D30. 1
Matrices in the Theory of Signed Simple Graphs
, 2008
"... Abstract. I discuss the work of many authors on various matrices used to study signed graphs, concentrating on usual (integral) and unusual (Abelson–Rosenberg) adjacency and incidence matrices and the closely related topics of Kirchhoff (‘Laplacian’) matrices, line graphs, and very strong regularity ..."
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Cited by 1 (0 self)
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Abstract. I discuss the work of many authors on various matrices used to study signed graphs, concentrating on usual (integral) and unusual (Abelson–Rosenberg) adjacency and incidence matrices and the closely related topics of Kirchhoff (‘Laplacian’) matrices, line graphs, and very strong regularity.
Matrices in the Theory of Signed Simple Graphs (Outline)
, 2008
"... This is an expository survey of the uses of matrices in the theory of simple graphs with signed edges. A signed simple graph is a graph, without loops or parallel edges, in which every edge has been declared positive or negative. For many purposes the most significant thing about a signed graph is n ..."
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This is an expository survey of the uses of matrices in the theory of simple graphs with signed edges. A signed simple graph is a graph, without loops or parallel edges, in which every edge has been declared positive or negative. For many purposes the most significant thing about a signed graph is not the actual edge signs, but the sign of each circle (or ‘cycle ’ or ’circuit’), which is the product of the signs of its edges. This fact is manifested in simple operations on the matrices I will present. I treat three kinds of matrices of a signed graph, all of them direct generalizations of familiar matrices from ordinary, unsigned graph theory. The first is the adjacency matrix. The adjacency matrix of an ordinary graph has 1 for adjacent vertices; that of a signed graph has +1 or −1, depending on the sign of the connecting edge. The adjacency matrix leads to questions about eigenvalues and strongly regular signed graphs. The second matrix is the vertexedge incidence matrix. There are two kinds of incidence matrix of a graph (without signs). The unoriented incidence matrix has two 1’s in each column, corresponding to the endpoints of the edge whose column it is. The oriented incidence matrix has a +1 and a −1 in each column. For a signed graph, there are both kinds of columns, the former corresponding to a negative edge and the latter to a positive edge. Finally, there is the Kirchhoff or Laplacian matrix. This is the adjacency matrix with signs reversed, and with the degrees of the vertices inserted in the diagonal. The Kirchhoff matrix equals the incidence matrix times its transpose. If we multiply in the other order, the transpose times the incidence matrix, we get the adjacency matrix of the line graph, but with 2’s in the diagonal. All this generalizes ordinary graph theory. Indeed, much of graph theory generalizes to signed graphs, while much—though not all—signed graph theory consists of generalizing facts about unsigned graphs. As this is a survey, I will give very few proofs. As it is an outline, I will give few references; they will be added to the final paper.
Six Signed Petersen Graphs
"... The Petersen graph with signed edges makes a fascinating example of many aspects of signed graph theory. I will show that there are exactly six essentially different ways to sign P, find their automorphisms, show some ways in which their signs matter, color them, and mention two applications of sign ..."
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The Petersen graph with signed edges makes a fascinating example of many aspects of signed graph theory. I will show that there are exactly six essentially different ways to sign P, find their automorphisms, show some ways in which their signs matter, color them, and mention two applications of signed graphs in which signed Petersen graphs have not yet made an appearance. The Petersen graph is P = (V, E) with vertex set V = {vij: 1 ≤ i < j ≤ 5} and edge set E = {vijvkl: {i, j} ∩ {k, l} = ∅}. A signed graph is a pair Σ: = (Γ, σ) where Γ is a graph and σ: E(Γ) → {+1, −1} is an (edge) signature that labels each edge positive or negative. Hence, a signed Petersen graph is (P, σ); two examples are +P: = (P, +1), where every edge is positive, and −P: = (P, −1), where every edge is negative. The sign of a circle (cycle, circuit, polygon) C is σ(C): = the product of the signs of the edges in C. The most essential fact about a signed graph is the set of circles that have negative sign. If this set is empty we call the signed graph balanced. Such a signed graph is equivalent to an unsigned graph in most ways. Harary [4] introduced
Modeling of Organizational DecisionProcesses
, 1982
"... Two fundamental differences exist between models of the organizational decision process based on participant recollection and those based on archival data or field observation. These differences are discussed and sources of bias in participant recollections are outlined. Finally, suggestions for min ..."
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Two fundamental differences exist between models of the organizational decision process based on participant recollection and those based on archival data or field observation. These differences are discussed and sources of bias in participant recollections are outlined. Finally, suggestions for minimizing these biases in decision process research are It has been argued (Katz, 1953; Mintzberg, Raisinghani, and Theoret, 1976, p. 248) that participant recollection provides the only satisfactory data on the decisionmaking process. Examination of archival data and field observation by researchers is sometimes seen as inappropriate because