scholarly journals Sequential Information Design

Econometrica ◽  
2020 ◽  
Vol 88 (6) ◽  
pp. 2575-2608
Author(s):  
Laura Doval ◽  
Jeffrey C. Ely
Keyword(s):  
Incomplete Information ◽  
Private Information ◽  
Information Structure ◽  
Information Design ◽  
Extensive Form ◽  
Main Concept ◽  
Sequential Information

We study games of incomplete information as both the information structure and the extensive form vary. An analyst may know the payoff‐relevant data but not the players' private information, nor the extensive form that governs their play. Alternatively, a designer may be able to build a mechanism from these ingredients. We characterize all outcomes that can arise in an equilibrium of some extensive form with some information structure. We show how to specialize our main concept to capture the additional restrictions implied by extensive‐form refinements.

scholarly journals Information Design: A Unified Perspective

2019 ◽  
Vol 57 (1) ◽  
pp. 44-95 ◽  
Author(s):  
Dirk Bergemann ◽  
Stephen Morris
Keyword(s):  
Incomplete Information ◽  
Recent Work ◽  
Design Problem ◽  
Information Structure ◽  
Large Body ◽  
Information Design ◽  
Information Structures ◽  
Literal Interpretation ◽  
Uncertain Payoffs ◽  
Metaphorical Interpretation

Given a game with uncertain payoffs, information design analyzes the extent to which the provision of information alone can influence the behavior of the players. Information design has a literal interpretation, under which there is a real information designer who can commit to the choice of the best information structure (from her perspective) for a set of participants in a game. We emphasize a metaphorical interpretation, under which the information design problem is used by the analyst to characterize play in the game under many different information structures. We provide an introduction to the basic issues and insights of a rapidly growing literature in information design. We show how the literal and metaphorical interpretations of information design unify a large body of existing work, including that on communication in games (Myerson 1991), Bayesian persuasion (Kamenica and Gentzkow 2011), and some of our own recent work on robust predictions in games of incomplete information. ( JEL C70, D82, D83)

scholarly journals Information Design, Bayesian Persuasion, and Bayes Correlated Equilibrium

2016 ◽  
Vol 106 (5) ◽  
pp. 586-591 ◽  
Author(s):  
Dirk Bergemann ◽  
Stephen Morris
Keyword(s):  
Incomplete Information ◽  
Information Structure ◽  
Information Design ◽  
Correlated Equilibrium

A set of players have preferences over a set of outcomes. We consider the problem of an “information designer” who can choose an information structure for the players to serve his ends, but has no ability to change the mechanism (or force the players to make particular action choices). We describe a unifying perspective for information design. We consider a simple example of Bayesian persuasion with both an uninformed and informed receiver. We extend information design to many player and relate it to the literature on incomplete information correlated equilibrium.

Sequential Preference Revelation in Incomplete Information Settings

2021 ◽  
Vol 13 (1) ◽  
pp. 116-147
Author(s):  
James Schummer ◽  
Rodrigo A. Velez
Keyword(s):  
Incomplete Information ◽  
Real World ◽  
Private Information ◽  
Allocation Rules ◽  
Is Strategy ◽  
Strategy Proof ◽  
Simultaneous Move

Strategy-proof allocation rules incentivize truthfulness in simultaneous move games, but real world mechanisms sometimes elicit preferences sequentially. Surprisingly, even when the underlying rule is strategy-proof and nonbossy, sequential elicitation can yield equilibria where agents have a strict incentive to be untruthful. This occurs only under incomplete information, when an agent anticipates that truthful reporting would signal false private information about others’ preferences. We provide conditions ruling out this phenomenon, guaranteeing all equilibrium outcomes to be welfare-equivalent to truthful ones. (JEL C73, D45, D82, D83)

Selective crowdsourcing with various type task: models and analysis

2018 ◽  
Vol 13 (4) ◽  
pp. 815-839 ◽  
Author(s):  
Qinglong Gou ◽  
Fangdi Deng ◽  
Yanyan He
Keyword(s):  
Incomplete Information ◽  
Private Information ◽  
Marginal Cost ◽  
Design Methodology ◽  
Learning Effect ◽  
Task Type ◽  
Expected Payoff ◽  
Content Type ◽  
The Past ◽  
Winner Takes All

Purpose Selective crowdsourcing is an important type of crowdsourcing which has been popularly used in practice. However, because selective crowdsourcing uses a winner-takes-all mechanism, implying that the efforts of most participants except the final winner will be just in vain. The purpose of this paper is to explore why this costly mechanism can become a popularity during the past decade and which type of tasks can fit this mechanism well. Design/methodology/approach The authors propose a game model between a sponsor and N participants. The sponsor is to determine its reward and the participants are to optimize their effort-spending strategy. In this model, each participant's ability is the private information, and thus, all roles in the system face incomplete information. Findings The results of this paper demonstrate the following: whether the sponsor can obtain a positive expected payoff are determined by the type of tasks, while the complex tasks with a strong learning effect is more suitable to selective crowdsourcing, as for the other two types of task, the sponsor cannot obtain a positive payoff, or can just gain a rather low payoff; besides the task type, the sponsor's efficiency in using the solutions and the public's marginal cost also influence the result that whether the sponsor can obtain a positive surplus from the winner-takes-all mechanism. Originality/value The model presented in this paper is innovative by containing the following characteristics. First, each participant's ability is private information, and thus, all roles in the system face incomplete information. Second, the winner-takes-all mechanism is used, implying that the sponsor's reward will be entirely given to the participant with the highest quality solution. Third, the sponsor's utility from the solutions, as well as the public's cost to complete the task, are both assumed as functions just satisfying general properties.

scholarly journals Spillovers and R&D Incentive under Incomplete Information

2018 ◽  
Vol 6 (1-2) ◽  
pp. 50-65 ◽  
Author(s):  
Rittwik Chatterjee ◽  
Srobonti Chattopadhyay ◽  
Tarun Kabiraj
Keyword(s):  
Distribution Function ◽  
Incomplete Information ◽  
Private Information ◽  
Complete Information ◽  
Jel Classification ◽  
General Distribution ◽  
Two Stage ◽  
Stage Game ◽  
Alternative Scenarios

Spillovers of R&D outcome affect the R&D decision of a firm. The present paper discusses the R&D incentives of a firm when the extent of R&D spillover is private information to each firm. We construct a two-stage game involving two firms when the firms first decide simultaneously whether to invest in R&D or not, then they compete in quantity. Assuming general distribution function of firm types we compare R&D incentives of firms under alternative scenarios based on different informational structures. The paper shows that while R&D spillovers reduce R&D incentives under complete information unambiguously, however, it can be larger under incomplete information. JEL Classification: D43, D82, L13, O31

scholarly journals Information Design

2019 ◽  
Vol 11 (4) ◽  
pp. 151-185 ◽  
Author(s):  
Ina Taneva
Keyword(s):  
Information Structure ◽  
Information Design ◽  
Signal Distribution ◽  
Private Signals ◽  
Optimal Information

A designer commits to a signal distribution that is informative about a payoff-relevant state. Conditional upon the privately observed signals, agents take actions that affect their payoffs as well as those of the designer. We show how to derive the (designer) optimal information structure in static finite environments. We fully characterize it in a symmetric binary setting for a parameterized game. In this environment, conditionally independent private signals are never strictly optimal. (JEL C72, D78, D82, D83)

Strategic Interaction and Interstate Crises: A Bayesian Quantal Response Estimator for Incomplete Information Games

2008 ◽  
Vol 16 (3) ◽  
pp. 250-273 ◽  
Author(s):  
Justin Esarey ◽  
Bumba Mukherjee ◽  
Will H. Moore
Keyword(s):  
Asymmetric Information ◽  
Incomplete Information ◽  
Learning Process ◽  
Private Information ◽  
Statistical Treatment ◽  
Quantal Response ◽  
Crisis Bargaining ◽  
Incomplete Information Games ◽  
Information Characteristics ◽  
Information Games

Private information characteristics like resolve and audience costs are powerful influences over strategic international behavior, especially crisis bargaining. As a consequence, states face asymmetric information when interacting with one another and will presumably try to learn about each others' private characteristics by observing each others' behavior. A satisfying statistical treatment would account for the existence of asymmetric information and model the learning process. This study develops a formal and statistical framework for incomplete information games that we term the Bayesian Quantal Response Equilibrium Model (BQRE model). Our BQRE model offers three advantages over existing work: it directly incorporates asymmetric information into the statistical model's structure, estimates the influence of private information characteristics on behavior, and mimics the temporal learning process that we believe takes place in international politics.

scholarly journals Solving Large Extensive-Form Games with Strategy Constraints

2019 ◽  
Vol 33 ◽  
pp. 1861-1868
Author(s):  
Trevor Davis ◽  
Kevin Waugh ◽  
Michael Bowling
Keyword(s):  
Private Information ◽  
Imperfect Information ◽  
Risk Mitigation ◽  
Solution Concept ◽  
Optimal Strategies ◽  
Linear Constraints ◽  
Convex Constraints ◽  
Extensive Form ◽  
Extensive Form Games ◽  
Large Extensive Form Games

Extensive-form games are a common model for multiagent interactions with imperfect information. In two-player zerosum games, the typical solution concept is a Nash equilibrium over the unconstrained strategy set for each player. In many situations, however, we would like to constrain the set of possible strategies. For example, constraints are a natural way to model limited resources, risk mitigation, safety, consistency with past observations of behavior, or other secondary objectives for an agent. In small games, optimal strategies under linear constraints can be found by solving a linear program; however, state-of-the-art algorithms for solving large games cannot handle general constraints. In this work we introduce a generalized form of Counterfactual Regret Minimization that provably finds optimal strategies under any feasible set of convex constraints. We demonstrate the effectiveness of our algorithm for finding strategies that mitigate risk in security games, and for opponent modeling in poker games when given only partial observations of private information.

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