1. Network Theory

2. Some Network Terminology Each case can be thought of as a vertex or node An arc i  j = case i cites case j in its majority opinion (directed or two-mode network) An arc from case i to case j represents an outward citation for case i an inward citation for case j A tie i  j = nodes are connected to one another (bilateral or symmetric network) Total arcs/ties leading to and from each vertex is the degree in degree = total inward citations out degree = total outward citations

3. Clustering Coefficient What is the probability that your friends are friends with each other? Network level Count total number of transitive triples in a network and divide by total possible number Ego level For ego-centered measure, divide total ties between friends by total possible number

4. Degree Centrality Degree centrality = number of inward citations (Proctor and Loomis 1951; Freeman 1979) InfoSynthesis uses this to choose cases for its CD-ROM containing the 1000 “most important” cases decided by the Supreme Court However, treats all inward citations the same Suppose case a is authoritative and case z is not Suppose case a  i and case z  j Implies i is more important than j

5. Eigenvector Centrality: An Improvement Eigenvector centrality estimates simultaneously the importance of all cases in a network (Bonacich 1972) Let A be an n x n adjacency matrix representing all citations in a network such that a ij = 1 if the ith case cites the jth case and 0 otherwise Self-citation is not permitted, so main diagonal contains all zeros

6. Eigenvector Centrality: An Improvement Let x be a vector of importance measures so that each case’s importance is the sum of the importance of the cases that cite it: x i = a 1i x 1 + a 2i x 2 + … + a ni x n or x = A T x Probably no nonzero solution, so we assume proportionality instead of equality: λx i = a 1i x 1 + a 2i x 2 + … + a ni x n or λx = A T x Vector of importance scores x can now be computed since it is an eigenvector of the eigenvalue λ

7. Problems with Eigenvector Centrality Technical many court cases not cited so importance scores are 0 0 score cases add nothing to importance of cases they cite citation is time dependent, so measure inherently biases downward importance of recent cases Substantive assumes only inward citations contain information about importance some cases cite only important precedents while others cast the net wider, relying on less important decisions

8. Well-Grounded Cases How well-grounded a case is in past precedent contains information about the cases it cites Suppose case h is well-grounded in authoritative precedents and case z is not Suppose case h  i and case z  j Implies i is more authoritative than j

9. Hubs and Authorities Recent improvements in internet search engines (Kleinberg 1998) have generated an alternative method A hub cites many important decisions Helps define which decisions are important An authority is cited by many well-grounded decisions Helps define which cases are well-grounded in past precedent Two-way relation well-grounded cases cite influential decisions and influential cases are cited by decisions that are well-grounded

10. Hub and Authority Scores Let x be a vector of authority scores and y a vector of hub scores each case’s inward importance score is proportional to the sum of the outward importance scores of the cases that cite it: λ x x i = a 1i y 1 + a 2i y 2 + … + a ni y n or x = A T y each case’s outward importance score is proportional to the sum of the outward impmortance scores of the cases that it cites: λ y y i = a i1 x 1 + a i2 x 2 + … + a in x n or y = Ax Equations imply λ x x = A T Ax and λ y y = AA T y Importance scores computed using eigenvectors of principal eigenvalues λ x and λ y

11. Closeness Centrality Sabidussi 1966 inverse of the average distance from one legislator to all other legislators let  ij denote the shortest distance from i to j Closeness is

12. Closeness Centrality Rep. Cunningham1.04 Rep. Rogers3.25

13. Betweeness Centrality Freeman 1977 identifies individuals critical for passing support/information from one individual to another in the network let  ik represent the number of paths from legislator i to legislator k let  ijk represent the number of paths from legislator i to legislator k that pass through legislator j Betweenness is

14. Large Scale Social Networks Sparse Average degree << size of the network Clustered High probability that one person’s acquaintances are acquainted with one another (clustering coefficient) Small world Short average path length “Six degrees of separation” (Milgram 1967)

15. Large Scale Social Network Data

16. Citations in High Energy Physics

17. Judicial Citations

18. Scientific and Judicial Citations Unifying property is the degree distribution P(k) = probability paper has exactly k citations Degree distributions exhibit power-law tail Common to many large scale networks Albert and Barabasi 2001 Common to scientific citation networks Redner 1998; Vazquez 2001 Suggests similar processes Academics may be as strategic as judges!

19. The Watts-Strogatz (WS) Model (Nature 1998) Order Chaos “Real” Social Network

20. Preferential Attachment and the Scale Free Model Barabasi and Albert, Science 1999 Add new nodes to a network one by one, allow them to “attach” to existing nodes with a probability proportional to their degree Yields scale-free degree distribution

21. Hierarchical Networks Ravasz and Barabasi 2003

22. Identifying Networks

23. Turnout in a Small World Social Logic of Politics 2005, ed. Alan Zuckerman Why do people vote? How does a single vote affect the outcome of an election? How does a single turnout decision affect the turnout decisions of one’s acquaintances?

24. Pivotal Voting Literature Most models assume independence between voters Decision-theoretic models Downs 1957; Tullock 1967; Riker and Ordeshook 1968; Beck 1974; Ferejohn and Fiorina 1974; Fischer 1999 Empirical models Gelman, King, Boscardin 1998; Mulligan and Hunter 2001 Game theoretic models imply negative dependence between voters Ledyard 1982,1984; Palfrey and Rosenthal 1983, 1985; Meyerson 1998; Sandroni and Feddersen 2006

25. Social Voting Literature Turnout is positively dependent between spouses ( Glaser 1959; Straits 1990 ) between friends, family, and co-workers Lazarsfeld et al 1944; Berelson et al 1954; Campbell et al 1954; Huckfeldt and Sprague 1995; Kenny 1992; Mutz and Mondak 1998; Beck et al 2002 Influence matters many say they vote because their friends and relatives vote (Knack 1992) Mobilization increases turnout Organizational (Wielhouwer and Lockerbie 1994; Gerber and Green 1999, 2000a, 2000b) Individual -- 34% try to influence peers (ISLES 1996)

26. Turnout Cascades If turnout is positively dependent then changing a single turnout decision may cascade to many voters’ decisions, affecting aggregate turnout If political preferences are highly correlated between acquaintances, this will affect electoral outcomes This may affect the incentive to vote Voting to “set an example”

27. Small World Model of Turnout Assign each citizen an ideological preference and initial turnout behavior Place citizens in a WS network Randomly choose citizens to interact with their “neighbors” with a small chance of influence Hold an election Give one citizen “free will” to measure cascade

28. Simplifying Assumptions Social ties are Equal Bilateral Static Citizens are Non-strategic Sincere in their discussions

29. Model Analysis Analytic--to a point: Create Simulation Analyze Model Using: A Single Network Tuned to Empirical Data Several Networks for Comparative Analysis

30. Political Discussion Network Data 1986 South Bend Election Study (SBES) 1996 Indianapolis-St. Louis Election Study (ISLES) (Huckfeldt and Sprague) “Snowball survey” of “respondents” and “discussants” Respondent Discussant Discussant’s Discussant

31. Features of a Political Discussion Network Like the ISLES Size: 186 million, but limited to 100,000-1 million Degree: 3.15 (but truncated sample) Clustering: 0.47 for “talk” 0.61 for “know” Interactions: 20 (3/week, 1/3 political, 20 weeks in campaign) Influence Rate: 0.05 (consistent w/ 1st,2nd order turnout corr.) Preference Correlation: 0.66 for lib/cons, 0.47 for Dem/Rep

32. Results: Total Change in Turnout in a Social Network Like the ISLES

33. Net Favorable Change in Turnout in a Social Network Like the ISLES

34. Turnout Cascades Magnify the Effect of a Single Vote A single turnout decision changes the turnout decision of at least 3 other people increases the vote margin of one’s favorite candidate by at least 2 to 3 votes Turnout cascades increase the incentive to vote by increasing the pivotal motivation (Downs 1957) signaling motivation (Fowler & Smirnov 2007) duty motivation (Riker & Ordeshook 1967) Consistent with people who say they vote to “set an example”

35. Do Turnout Cascades Exist? Cascades increase with number of discussants But this correlates strongly with interest How does individual-level clustering affect the size of turnout cascades? Social capital literature suggests monotonic and increasing Individual Network Characteristics Turnout Cascades Intention to Influence and Turnout

36. Prediction: How Individual-Level Clustering Affects Simulated Turnout

37. What’s Going On? Clustering increases the number of paths of influence both within and beyond the group With a fixed number of acquaintances, clustering decreases the number of connections to the rest of the network BA C F G D E BA C F G D E BA C F G D E

38. Results: How Individual Clustering Affects Intention to Influence

39. How Individual Clustering Affects Intention to Vote

40. The Strength of Mixed Ties “Weak” ties may be more influential than “strong” ties because they permit influence between cliques (Grannovetter 1973) Evidence here suggests that a mixture of strong and weak ties maximizes the individual incentive to set an example by participating

41. Stylized Facts for Aggregate Turnout Turnout increases in: Number of contacts Wielhouwer and Lockerbie 1994; Ansolabehere and Snyder 2000; Gerber and Green 1999, 2000 Clustering of social ties Cox, Rosenbluth, and Thies 1998; Monroe 1977 Concentration of shared interests Busch and Reinhardt 2000; Brown, Jackson, and Wright 1999; Gray and Caul 2000; Radcliff 2001

42. Number of Contacts

43. Clustering of Social Ties

44. Concentration of Shared Interests

45. Implications Turnout Cascades & Rational Voting Turnout cascades magnify the incentive to vote by a factor of 3-10 Even so, not sufficient Explaining the Civic Duty Norm Establishing a norm of voting with one’s acquaintances can influence them to go to the polls People who do not assert such a duty miss a chance to influence people who share similar views, leading to worse outcomes for their favorite candidates

46. Implications Over-Reporting Turnout Strategic people may tell others they vote to increase the margin for their favorite candidates It is rational to do this without knowing anything about the candidates in the election! May explain over-reporting of turnout (Granberg and Holmberg 1991) Paradox: why would people ever say they don’t vote? Social Capital Bowling together is better for participation than bowling alone (Putnam 2000) BUT, who we bowl with is also important People concerned about participation should be careful to encourage a mix of strong and weak ties (Granovetter 1973)