The Structural Transformation of the Public Sphere: An Inquiry into a Category of Bourgeois Society

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model 3. Causal graphs Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators 5. Matching estimators of causal effects 6. Regression estimators of causal effects 7. Weighted regression estimators of causal effects Part IV. Estimating Causal Effects When Backdoor Conditioning Is Ineffective: 8. Self-selection, heterogeneity, and causal graphs 9. Instrumental-variable estimators of causal effects 10. Mechanisms and causal explanation 11. Repeated observations and the estimation of causal effects Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science.

Counterfactuals and Causal Inference: Methods and Principles for Social Research

The package fitdistrplus provides functions for fitting univariate distributions to different types of data (continuous censored or non-censored data and discrete data) and allowing different estimation methods (maximum likelihood, moment matching, quantile matching and maximum goodness-of-fit estimation). Outputs of fitdist and fitdistcens functions are S3 objects, for which specific methods are provided, including summary, plot and quantile. This package also provides various functions to compare the fit of several distributions to the same data set and can handle to bootstrap parameter estimates. Detailed examples are given in food risk assessment, ecotoxicology and insurance contexts.

fitdistrplus: An R Package for Fitting Distributions

Low voter turnout is a serious democratic problem for five reasons: (1) It means unequal turnout that is systematically biased against less well-to-do citizens. (2) Unequal turnout spells unequal political influence. (3) U.S. voter turnout is especially low, but, measured as percent of voting-age population, it is also relatively low in most other countries. (4) Turnout in midterm, regional, local, and supranational elections -- less salient but by no means unimportant elections -- tends to be especially poor. (5) Turnout appears to be declining everywhere. The inequality problem can be solved by institutional mechanisms that maximize turnout. One option is the combination of voter-friendly registration rules, proportional representation, infrequent elections, weekend voting, and holding less salient elections concurrently with the most important national elections. The other option, which can maximize turnout by itself, is compulsory voting. Its advantages far outweigh the normative and practical objections to it. This article has been removed from the eRepository website since it is now published: American Political Science Review vol. 91 (March 1997): 1-14.

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Unequal Participation: Democracy's Unresolved Dilemma

This introduction to the R package rgenoud is a modied version of Mebane and Sekhon (2011), published in the Journal of Statistical Software. That version of the introduction contains higher resolution gures. genoud is an R function that combines evolutionary algorithm methods with a derivativebased (quasi-Newton) method to solve dicult optimization problems. genoud may also be used for optimization problems for which derivatives do not exist. genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood) is nonlinear in the model’s parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to nd any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms) are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.

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Genetic Optimization Using Derivatives: The rgenoud Package for R

We show that the butterfly ballot used in Palm Beach County, Florida, in the 2000 presidential election caused more than 2,000 Democratic voters to vote by mistake for Reform candidate Pat Buchanan, a number larger than George W. Bush's certified margin of victory in Florida. We use multiple methods and several kinds of data to rule out alternative explanations for the votes Buchanan received in Palm Beach County. Among 3,053 U.S. counties where Buchanan was on the ballot, Palm Beach County has the most anomalous excess of votes for him. In Palm Beach County, Buchanan's proportion of the vote on election-day ballots is four times larger than his proportion on absentee (nonbutterfly) ballots, but Buchanan's proportion does not differ significantly between election-day and absentee ballots in any other Florida county. Unlike other Reform candidates in Palm Beach County, Buchanan tended to receive election-day votes in Democratic precincts and from individuals who voted for the Democratic U.S. Senate candidate. Robust estimation of overdispersed binomial regression models undernins much of the analysis.

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The Butterfly Did It: The Aberrant Vote for Buchanan in Palm Beach County, Florida

We describe a new computer program that combines evolutionary algorithm methods with a derivative-based, quasi-Newton method to solve difficult unconstrained optimization problems. The program, called GENOUD (GENetic Optimization Using Derivatives), effectively solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When a statistical model's estimating function (for example, a log-likelihood) is nonlinear in the model's parameters, the function to be optimized will usually not be globally concave and may contain irregularities such as saddlepoints or discontinuous jumps. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Or multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. We discuss the theoretical basis for expecting GENOUD to have a high probability of finding global optima. We conduct Monte Carlo experiments using scalar Normal mixture densities to illustrate this capability. We also use a system of four simultaneous nonlinear equations that has many parameters and multiple local optima to compare the performance of GENOUD to that of the Gauss-Newton algorithm in SAS's PROC MODEL.

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Genetic Optimization Using Derivatives

Voters have been coordinating their choices for president and House of Representatives in recent presidential election years, with each voter using a strategy that features policy moderation. Coordination is defined as a noncooperative rational expectations equilibrium among voters, in which each voter has both common knowledge and private information about the election outcome. Stochastic choice models estimated using individual-level NES data from 1976–96 support coordination versus a model in which voters act nonstrategically to moderate policy. The empirical coordinating model satisfies the fixed-point condition that defines the common knowledge expectation voters have about the outcome in the theoretical equilibrium. The proportion of voters who split their ticket in order to balance the House with the president has been small but large enough to affect election outcomes. Moderation has usually been based on voters' expectations that the president will be at least an equal of the House in determining postelection policy.

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Coordination, Moderation, and Institutional Balancing in American Presidential and House Elections

Election Forensics: The Second-digit Benford’s Law Test and Recent American Presidential Elections While the technology to conduct elections continues to be imperfect, it is useful to investigate methods for detecting problems that may occur. A method that seems to have many good properties is to test whether the second digits of reported vote counts occur with the frequencies specified by Benford’s Law. I illustrate use of this test by applying it to precinct-level votes reported in recent American presidential elections. The test is significant for votes reported from some notorious places. But the test is not sensitive to distortions we know significantly affected many votes. In particular, the test does not indicate problems for Florida in 2000. Regarding Ohio in 2004, the test does not overturn previous judgments that manipulation of reported vote totals did not determine the election outcome, but it does suggest there were significant problems in the state. The test is worth taking seriously as a statistical test for election fraud. Arguably we are not much closer than we were one hundred years ago to understanding how to administer elections that not only are secure and fair but are widely believed to be secure and fair. As long as there have been elections there have been election scandals, and certainly throughout the history of the United States (Gumbel 2005). Notoriously, serious defects in election administration produced the wrong outcome in the 2000 American election for president (Wand, Shotts, Sekhon, Mebane, Herron, and Brady 2001; Mebane 2004b). Responses to the 2000 election controversy have in some ways created as many problems as they have solved. In particular, the events of 2000 sparked a rush to replace older mechanical voting technologies with machines based on electronic computers. Some states made such changes on their own, notably Florida (MacManus 2004), while others were prompted to change by provisions of the Help America Vote Act of 2002 that made use of punchcard ballots and lever machines illegal and provided funds to help pay for their replacement. Debates about the accuracy and security of different voting technologies began in the nineteenth century and continue to the present (Saltman 2006). In 1934, Joseph Harris wrote about a primary defect of paper ballots: “The counting of paper ballots, often lasting far into the night, and made by tired and frequently incompetent persons, is highly conducive to mistakes and frauds. Many election officers and men in public life have realized the inherent defects of this procedure and have sought to remedy it” (Harris 1934, 261). But in 2006, Aviel Rubin lamented the insecurity of a wholly electronic system being used in Maryland: “All of the votes from our entire precinct were right there in the palm of my hand. I could have substituted those five [memory] cards with five identical but bogus cards from my pocket, changing all the ballots, because Diebold did not protect the data with appropriate cryptographic measures” (Rubin 2006, 256). It seems unlikely that technological developments alone will solve the problems and resolve the questions many have about election administration, at least not in the foreseeable future. While the technology to conduct elections continues to be imperfect, it is useful to investigate methods for detecting problems that may occur. The class of such methods I refer to as election forensics are based on statistical tools and are intended to examine elections after the fact. Election forensics focus on the recorded votes, asking whether there are significant anomalies. Do the votes relate to covariates in ways we should expect, or are some votes outliers (Mebane and Sekhon 2004)? Are there other regularities the votes should exhibit? The analysis by Wand et al.

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Walter R. Mebane

Papers

Genetic Optimization Using Derivatives: The rgenoud Package for R

The Butterfly Did It: The Aberrant Vote for Buchanan in Palm Beach County, Florida

Genetic Optimization Using Derivatives

Coordination, Moderation, and Institutional Balancing in American Presidential and House Elections

Election Forensics: The Second-digit Benford's Law Test and Recent American Presidential Elections