What Networks Tell US about Collective Action

Do Networks Facilitate Collective Action?John T. ScholzFlorida State University

Do Networks Facilitate Collective Action?

When individuals following their own interests lead to undesirable outcomes that noone wants, a collective action problem exists.

1Collective Action Problems:Great Cities from Dismal Swamps

Collective Action reflects the conflict between individual and collective rationality that lies at the heart of political science and its concern with governance. How can we BUILD GREAT CITIES AND PRODUCTIVE FARMS FROM DISMAL SWAMPS.OR, in the more modern version how can we restore great wetlands destroyed by dismal cities. 2Collective Action Cures:Authority

Over 3 centuries ago Thomas Hobbes argued that the coercive powers of centralized authority was required to cure collective action problems.3Collective Action Cures:Enlightened Self-Interest

It took two centuries for John Stuart Mill to suggest that enlightened self-interest could potentially replace state coercion. 4Collective Action Cures:Collaborative Institutions

and a full century for Ostrom and others to clarify institutional conditions that can foster collaboration. 5Collective Action Cures:Self-Organizing FederalismFeiock and Scholz, eds., Cambridge Press, 2010

HobbesMillOstromWe now are investigating a full range of facilitating mechanisms in federalist systems rangingfrom the Hobbesian authoritative agencies on the right through Ostroms partnerships and collaborative partnerships in the middleto the Millsian informal policy networks on the left. These informal policy networks are arguably the least understood but most important of all these mechanisms, since they are generally involved in all other mechanisms.

This century will hopefully clarify their role in governance of collective action problems

6Networks as Cures

Bonding Bridging Relationships Relationships

COOPERATION: Trust and commitmentStrong tiesReciprocity, transitivityCOORDINATION: InformationWeak ties Centrality, closeness, brokerageThe basic idea about Networks and Collective action is illustrated by the simple contrast between ego A on the left who links to alters that know each other and ego B on the right who does not.

Bonding relationships on the left enhance commitment, trust, norms. They provide social capital to resolve Collective Action Problems involving defection and free riding, such as prisoners dilemmas, public goods, common-pool resources.

Tie Strength => Strong, frequent, intense, multidimentional interactions with strong affinity for partnerNetwork Structures => Reciprocity, transitivity, redundant, overlapping ties.

Bridging relationships on the right enhance information transmission and resource exchanges. They are most useful to resolve coordination games with low risk of defection, such as color matching, Battle of the Sexes, Stag HuntTie Strength => weak tiesNetwork structures => betweenness centrality, closeness, brokerage

I will illustrate alternative research approaches to explore these hypotheses including field observations, agent-based models, and experimental research.7Field Study EvidenceBerardo and Scholz 2010

Stakeholders seek Bridging Capital

The first field study found that stakeholders in 10 water policy networks primarily consult with popular stakeholders that others are already consulting. These uncoordinated decisions tend to produce central coordinators in each estuary, presumably valuable for coordinating relatively low-risk policy choices. Stakeholders also seek reciprocal relations for bonding capital, but not transitive, clustered relationships.

8Field Study EvidenceBridging, Not Bonding, Increases Level of Collaboration Scholz,Berardo and Kile 2008Degree (log).36**(.06)Betweenness Centrality.22**(.04)Egonet Density-.06*(.03)

A second analysis in similar settings found that bridging capital was also most associated with collaborative activities: stakeholders with higher degree and with greater betweenness centralilty had higher levels of collaboration. Apparently, information is more important than credibility as a foundation for collaboration.

Observing networks and performance is expensive and error-prone, so the next study creates an artificial world of agent-based models with prespecified conditions.9Agent-Based Modeling EvidenceScholz and Wang 2006 Bridging increases the evolution of cooperation In this simulation study all agents play 2-person iterated prisoners dilemmas with everyone in different-sized populations. They learn about how well other agents do through preassigned information networks, and partially adopt the most successful strategies they see.

The graph shows that bridging in the information network is again more important than bonding in facilitating the evolution of cooperation. The size of the network and the average number of alters, or degree increase the level of cooperation. Clustering has a positive but insignificant effect.

Of course, agent based models may not adequately represent actual human behavior, so we next turn to laboratory experiments to investigate how networks and cooperation coevolve when subjects confront the voluntary dilemma. 10Experimental EvidenceVOLUNTARY DILEMMA- Subjects play repeated prisoners dilemma, but with a choice of partners14 subjects per sessionSubjects propose to any number of potential partners, and play PD if both partners propose in the period.Repeat for 20 rounds (known in advance).Cooperate (red) and Defect (black): Period 1

Here we will see the choices as they evolve over 20 periods for four sessions. The blue circles represent subjects, red lines represent cooperative choices, black lines represent choices to defect, and no line means that the subjects did not play in the period.

@@@At last, I thought, we found evidence that bonding relationships were critical for cooperation, at least in voluntary dilemmas. Cooperation was heavily concentrated in the clusters.

However, further analysis revealed that clustering did very little to elicit cooperation, as hypothesized in the social capital literature. Instead, cooperators located each other over time and preserved their ties. 12Cooperate (red) and Defect (black): Period 2

Cooperate (red) and Defect (black): Period 3

Cooperate (red) and Defect (black): Period 4

Cooperate (red) and Defect (black): Period 5

Cooperate (red) and Defect (black): Period 6

Cooperate (red) and Defect (black): Period 7

Cooperate (red) and Defect (black): Period 8

Cooperate (red) and Defect (black): Period 9

Cooperate (red) and Defect (black): Period 10

Cooperate (red) and Defect (black): Period 11

Cooperate (red) and Defect (black): Period 12

Cooperate (red) and Defect (black): Period 13

Cooperate (red) and Defect (black): Period 14

Cooperate (red) and Defect (black): Period 15

Cooperate (red) and Defect (black): Period 16

Note that the clusters of cooperation have been consolidated for many rounds, with the defect choices limited to what coauthor TK Ahn has called Nashville, the ghetto where all play the dominant defect strategy

We now start observing some endgame effects that, in the final round, indicate that those in the cooperators gated community are not pure altruists27Cooperate (red) and Defect (black): Period 17

Cooperate (red) and Defect (black): Period 18

Cooperate (red) and Defect (black): Period 19

Cooperate (red) and Defect (black): Period 20

The black last round indicates that the cooperators were not unconditional altruists!31They accomplish this pairing relatively efficiently by using Quit-for-tat, an extension of TFT that is always nice but quits any relationship after a defection. As indicated in this figure, nice strategies that were the most optimistic in aggressively seeking partners early and quickest to quit after a partner defected earned the highest payoffs!!! Clustering was just an artifact of the selection process

32The Network Century for Collective Action???Utilize multiple research methods and designsDevelop additional methods of field observationEmail and agency contact listsMedia Archives and issue networksInternet Crawlers and social mediaUtilize Measures directly related to conceptsNature of the problem (credibility vs resource flows)Extent of effects (ego network, alter network, full network)The Network Century for Collective ActionJohn T. ScholzFSUCooperation is favored when b/c>kOhtsuki, Hauert, Lieberman and Nowak, Nature 441: 502 (May 2006)

Bonding RelationshipsCooperation games with risk of defection Bonding relationships enhance commitment, trust, norms, providing social capital to resolve Collective Action Problems as represented by prisoners dilemma, public goods, common-pool resources

Tie Strength => Strong, frequent, intense, multidimensial interactions with strong affinity for partnerNetwork Structures => Reciprocity, transitivity, redundant, overlapping ties.

Current theory most developed for network characteristics as bonding social capital: Bonding relationships associated with cooperation games with risk of defection: prisoners dilemma, public goods, common-pool resources

Bonding relationships expected to enhance commitment, trust, norms, providing social capital to resolve Collective Action ProblemsStrength of the observed dyadic relationship => Strong, frequent, intense, multidimensial interactions with strong affinity for partner

The overall pattern or network structures=> Reciprocity, transitivity, redundant, clustering with overlapping ties, both for egonetworks for individuals with good bonding relationships and as a general characteristic of a full networks with strong social capital.

36Bridging RelationshipsCoordination games with low risk of defectionMatching, Battle of the Sexes, Stag Hunt

Tie Strength => Weak, infrequent as needed, unidimensional, with many partners and little affinityNetwork Structures, flows => Degree, Reach, Closeness, non-redundant, overlapping ties.Network Structures, Brokerage => Betweenness, structural holes, brokerageAlternatively, bridging relationships can provide advantages for other types of collective action problems: Coordination games with low risk of defectionMatching, Battle of the Sexes, Stag Hunt Bridging enhances information transmission, resource exchanges Tie Strength => Weak, infrequent as needed, unidimensional, with many partners and little affinity

Network Structures => Degree Centrality, Reach, Closeness, non-redundant, non-overlapping ties for rapid transmissionHigh scores mean most rapid receipt of information distributed randomly Betweenness, structural hole, brokerage for strategic positionsGain advantages as broker between others needing to exchange resources.

37Experimental EvidenceCooperative communities emerge when cooperators successfully find each other

Clusters do not change behavior, at least in the short run.

Remaining challenge: Can clustering enhance long-term cooperation when none exists???The implication is that endogenous network formation, or partner selection, is likely to be a critical factor in supporting cooperation in the short run. The biggest

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