Nicholas Weller

Knowledge and networks: An experimental test of how network knowledge affects coordination

Abstract Scholars in the social sciences use network theory to study a range of collective action problems. Often the goal is to identify how the structure of the network affects efforts to coordinate or cooperate, and research suggests that adding connections to a network can improve the performance of groups faced with such tasks. On the other hand, theory and empirics also suggest that additional connections can degrade the performance of a network. If connections can have negative effects then it is important to consider if there are alternatives to adding connections to a network that can also improve network performance. Because a primary function of connections in a network is to disseminate information, providing individuals with more information about the network may act as a substitute for adding connections to a network. We test experimentally whether providing subjects with more information about the structure of networks can improve coordination. We find that a more complete view of the network leads to faster coordination, but the magnitude of this effect depends on network structure. These results suggest that changing what actors know about a network can improve outcomes without having to add connections that may impede overall performance.

Does more connectivity help groups to solve social problems

A growing literature on human networks suggests that the way we are connected influences both individual and group outcomes. Recent experimental studies in the social and computer sciences have claimed that higher network connectivity helps individuals solve coordination problems. However, this is not always the case, especially when we consider complex coordination tasks; we demonstrate that networks can have both constraining edges that inhibit collective action and redundant edges that encourage it. We show that the constraints imposed by additional edges can impede coordination even though these edges also increase communication. By contrast, edges that do not impose additional constraints facilitate coordination, as described in previous work. We explain why the negative effect of constraint trumps the positive effect of communication by analyzing coordination games as a special case of widely-studied constraint satisfaction problems. The results help us to understand the importance of problem complexity and network connections, and how different types of connections can influence real-world coordination.

Income taxation and the validity of state capacity indicators

Author(s): Rogers, Melissa Ziegler; Weller, Nicholas | Abstract: AbstractState capacity is a key concept for research in public policy and political science. Despite its importance, there is no broadly accepted measure of state capacity in the existing literature, and frequently used measures of capacity have not been examined for their validity. We begin with an explicit definition of state capacity – the states ability to implement public policy – and connect this definition to a measurable outcome of state capacity – the states taxation of income. We show that this measure, income taxes as a percentage of total tax revenue, is a useful indicator of state capacity and meets higher standards of measurement validity than other tax-based indicators. We also compare our measure to the most common existing indicators of state capacity to show that income taxation is a better theoretical and statistical measure of states’ effectiveness in policy implementation.

Diffusion in Direct Democracy: The Effect of Political Information on Proposals for Tax and Expenditure Limits in the U.S. States

Many theories of policy diffusion contend that the flow of information is the driving force in the diffusion process. Prior scholarship has identified at least two types of information: information about policy and information about political viability. Few empirical approaches have been able to distinguish between these separate mechanisms. We argue that an analysis of policy proposals can untangle political information from policy-based information. We employ our strategy with data on the proposal of tax and expenditure limits (TELs) in the U.S. states since 1970 through direct democracy. We find that states in close geographic proximity to states that have rejected TELs are significantly less likely to propose TELs themselves. Since this event does not reveal information about policy effectiveness, we conclude that information about political viability systematically diffuses from state to state at the proposal stage of policy making.

Diffusion in Direct Democracy

Many theories of policy diffusion contend that the flow of information is the driving force in the diffusion process. Prior scholarship has identified at least two types of information: information about policy and information about political viability. Few empirical approaches have been able to distinguish between these separate mechanisms. The authors argue that an analysis of policy proposals can untangle political information from policy-based information. They employ their strategy with data on the proposal of tax and expenditure limits (TELs) in the U.S. states since 1970 through direct democracy. The authors find that states in close geographic proximity to states that have rejected TELs are significantly less likely to propose TELs themselves. Since this event does not reveal information about policy effectiveness, the authors conclude that information about political viability systematically diffuses from state to state at the proposal stage of policy making.

Pathway Analysis and the Search for Causal Mechanisms

The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting cases to study causal mechanisms. We outline a general approach for selecting cases for pathway analysis: a mode of qualitative research that is part of a mixed-method research agenda, which seeks to (1) understand the mechanisms or links underlying an association between some explanatory variable, X1, and an outcome, Y, in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X1/Y relationship. The gist of our approach is that researchers should choose cases for comparison in light of two criteria. The first criterion is the expected relationship between X1/Y, which is the degree to which cases are expected to feature the relationship of interest between X1 and Y. The second criterion is variation in case characteristics or the extent to which the cases are likely to feature differences in characteristics that can facilitate hypothesis generation. We demonstrate how to apply our approach and compare it to a leading example of pathway analysis in the so-called resource curse literature, a prominent example of a correlation featuring a nonlinear relationship and multiple causal mechanisms.

Game-Theoretic Target Selection in Contagion-based Domains

Many strategic actions carry a ‘contagious’ component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, nonlocal impact. To address this new class of security games, we combine recent research in influence blocking maximization with a double oracle approach and create novel heuristic oracles to generate mixed strategies for a real-world leadership network from Afghanistan, synthetic leadership networks, and scale-free graphs. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected scale-free graphs.

Testing the foundations of quantal response equilibrium

Quantal response equilibrium (QRE) has become a popular alternative to the standard Nash equilibrium concept in game theoretic applications. It is well known that human subjects do not regularly choose Nash equilibrium strategies. It has been hypothesized that subjects are limited by strategic uncertainty or that subjects have broader social preferences over the outcome of games. These two factors, among others, make subjects boundedly-rational. QRE, in essence, adds a logistic error function to the strict, knife-edge predictions of Nash equilibria. What makes QRE appealing, however, also makes it very difficult to test, because almost any observed behavior may be consistent with different parameterizations of the error function. We present the first steps of a research program designed to strip away the underlying causes of the strategic errors thought to be modeled by QRE. If these causes of strategic error are correct explanations for the deviations, then their removal should enable subjects to choose Nash equilibrium strategies. We find, however, that subjects continue to deviate from predictions even when the reasons presumed by QRE are removed. Moreover, the deviations are different for each and every game, and thus QRE would require the same subjects to have different error parameterizations. While we need more expansive testing of the various causes of strategic error, in our judgment, therefore, QRE is not useful at predicting human behavior, and is of limited use in explaining human behavior across even a small range of similar decisions.

The mythology of game theory

Non-cooperative game theory is at its heart a theory of cognition, specifically a theory of how decisions are made. Game theorys leverage is that we can design different payoffs, settings, player arrays, action possibilities, and information structures, and that these differences lead to different strategies, outcomes, and equilibria. It is well-known that, in experimental settings, people do not adopt the predicted strategies, outcomes, and equilibria. The standard response to this mismatch of prediction and observation is to add various psychological axioms to the game-theoretic framework. Regardless of the differing specific proposals and results, game theory uniformly makes certain cognitive assumptions that seem rarely to be acknowledged, much less interrogated. Indeed, it is not widely understood that game theory is essentially a cognitive theory. Here, we interrogate those cognitive assumptions. We do more than reject specific predictions from specific games. More broadly, we reject the underlying cognitive model implicitly assumed by game theory.

Making Talk Cheap (and Problems Easy): How Legal and Political Institutions Can Facilitate Consensus

In many legal, political, and social settings, people must reach a consensus before particular outcomes can be achieved and failing to reach a consensus may be costly. In this article, we present a theory and conduct experiments that take into account the costs associated with communicating, as well as the difficulty of the decisions that groups make. We find that when there is even a small cost (relative to the potential benefit) associated with sending information to others and/or listening, groups are much less likely to reach a consensus, primarily because they are less willing to communicate with one another. We also find that difficult problems significantly reduce group members’ willingness to communicate with one another and, therefore, hinder their ability to reach a consensus.