Contest Organizer Research Articles

공공재적 상(prizes)을 갖는 Tullock 집단 경합 모형에 대한 소고

The rent-seeking contest theory of Tullock has been deriving a lot of studies on competitions between individuals and/or groups in various settings. Among them, we reviews several works that examine Tullock group contests with group-specific public-good prizes in order to enhance our understanding about the effect of the degree of substitution or complementarity between members in a group on their effort incentives for their group’s winning. We also reviews studies on the optimal degree of substitution (complementarity) that maximizes the objective function of a contest organizer and group members’ payoffs, respectively, and endogenous formation of groups in Tullock contest setting as well. This paper may provide helpful knowledge and information that can be referred to design solutions for alleviating wasteful resources spent on rent-seeking behavior in our society.

Costly information providing in binary contests

Contests are commonly used as a mechanism for eliciting effort and participation in multi-agent settings. Naturally, and much like with various other mechanisms, the information provided to the agents prior to and throughout the contest fundamentally influences its outcomes. In this paper we study the problem of information providing whenever the contest organizer does not initially hold the information and obtaining it is potentially costly. As the underlying contest mechanism for our model we use the binary contest, where contestants’ strategy is captured by their decision whether or not to participate in the contest in the first place. Here, it is often the case that the contest organizer can proactively obtain and provide contestants information related to their expected performance in the contest. We provide a comprehensive equilibrium analysis of the model, showing that even when such information is costless, it is not necessarily the case that the contest organizer will prefer to obtain and provide it to all agents, let alone when the information is costly.

The “sports wars”: A contest theory approach to cities hosting game

The competition among cities to host a sports team or a large-scale international sports event is modeled as a winner-pay contest with an entry fee. In the first stage, each contestant (city) decides whether to pay the entry fee (infrastructure required by the team, for example), which allows it to participate in the second stage, that is, the actual contest. We show that the contest organizer’s choice of the optimal entry fee does not depend on the number of contestants. Furthermore, in some cases, the result is a form of zero-sum game, in which the sports team or event organizer is the main beneficiary. The findings shed light on this type of competition and under what conditions on the entry fee a city might benefit from hosting a sports team or large-scale international sports event.

The Influence of Status on Evaluations: Evidence from Online Coding Contests

In many instances, online contest platforms rely on contestants to ensure submission quality. This scalable evaluation mechanism offers a collective benefit. However, contestants may also leverage it to achieve personal, competitive benefits. Our study examines this tension from a status-theoretic perspective, suggesting that the conflict between competitive and collective benefits, and the net implication for evaluation efficacy, is influenced by contestants’ status. On the one hand, contestants of lower status may be viewed as less skilled and hence more likely to make mistakes. Therefore, low-status contestants may attract more evaluations if said evaluations are driven predominantly by an interest in collective benefits. On the other hand, if evaluations are driven largely by an interest in personal, competitive benefits, a low-status contestant makes for a less attractive target and hence may attract fewer evaluations. We empirically test these competing possibilities using a dataset of coding contests from Codeforces. The platform allows contestants to assess others’ submissions and improve evaluations (a collective benefit) by devising test cases (hacks) in addition to those defined by the contest organizer. If a submission is successfully hacked, the hacker earns additional points, and the target submission is eliminated from the contest (a competitive benefit). We begin by providing qualitative evidence based on semi-structured interviews conducted with contestants spanning the status spectrum at Codeforces. Next, we present quantitative evidence exploiting a structural change at Codeforces wherein many contestants experienced an arbitrary status reduction unrelated to their performance because of sudden changes to the platform’s color-coding system around contestant ratings. We show that status-loser contestants received systematically more evaluations from other contestants, absent changes in their short-run submission quality. Finally, we show that the excess evaluations allocated toward affected contestants were less effective, indicating status-driven evaluations as potentially less efficacious. We discuss the implications of our findings for managing evaluation processes in online contests.

Elimination contests with collusive team players

AbstractWe consider a standard two‐stage elimination (Tullock) contest where multiple (team) players can perfectly and publicly collude with each other throughout. We analyze and compare equilibrium outcomes under various seedings where the collusive players meet or are separated in the group stage. We identify the impact of collusion on the contest organizer and non‐collusive players, as well as the organizer's optimal seeding. We find that collusion, while always undermining fairness of the competition, can hurt or benefit the organizer, depending on the discriminatory powers of the two stages. We also discuss issues such as sequential group‐stage competitions, comparison between the elimination contest and the corresponding one‐shot contest, secret collusion, and large discriminatory powers.

Blind analysis on the shaking table test of a 7-story reinforced concrete building using a detailed finite element model

Blind analysis conducted with hidden experimental data is needed to examine the accuracy of numerical model results without any arbitrary tunning. For this purpose, the National Center for Research on Earthquake Engineering in Taiwan held an international blind analysis contest for the shaking table test of a half-scale 7-story reinforced concrete building. Our analysis obtained using a detailed finite element (FE) model won the 1st place out of 66 teams. The detailed FE model of the entire building shape was composed of solid elements for concrete parts and beam elements for reinforced bars, and bond slip was defined between the solid and beam elements. A conventional framing model was also developed to obtain the reference data. The detailed FE model reproduced the structural characteristics of the building with the hidden experimental data. The accuracy of the FE model was superior to that of the frame model in large deformation ranges. Because the supplied experimental data from the contest organizer may include the effect of shaking table rocking, removing the rocking effect can further improve the FE model.

Equilibrium player choices in team contests with multiple pairwise battles

We consider games in which team leaders strategically choose the order of players sent to the battlefield in majoritarian team contests with multiple pairwise battles as in Fu et al. (2015). We consider one-shot order-choice games and battle-by-battle sequential player choice games. We show that as long as the number of players on each team is the same as the number of battles, the equilibrium winning probability of a team and the ex ante expected effort of each player in a multi-battle contest are independent of whether players' assignments are one-shot or battle-by-battle sequential. This equilibrium winning probability and ex ante expected total effort coincide with those where the player matching is chosen totally randomly with an equal probability lottery by the contest organizer. Finally, we show how player choices add subtleties to the equivalence result by examples.

When to disclose the number of contestants: Theory and experimental evidence

This paper investigates whether it is optimal for a contest organizer to disclose the actual number of contestants for total effort optimization when entry in a contest is costly and endogenous. Our model suggests that in a Tullock (1980) contest, the answer depends on the convexity of the cost of effort function. Even though the equilibrium entry rate and rent dissipation are invariant to the disclosure policy, disclosing (concealing) the actual number of entrants can lead to a higher total effort when the cost function is concave (convex). To test these theoretical predictions, we design a 2×3 between-subjects laboratory experiment using lottery contests. We vary the disclosure policy (fully disclosed vs. fully concealed) in one dimension and the curvature of the cost of effort function (concave, linear, or convex) in the other dimension. Our results are largely consistent with the theoretical predictions regarding the optimal disclosure policy, despite the presence of moderate over-entry and over-exertion behavior that is commonly observed in experimental studies of contests.

Entry regulations and optimal prize allocation in parallel contests

In parallel contests, the contest organizer controls the entry of heterogeneous contestants by regulating access to the contests and determining the prize allocation across contests. The organizer can prevent a contestant from entering more than one contest. I show that the organizer allows entry to multiple contests and uniquely sets identical prizes across contests to maximize aggregate effort in all contests. Independent of the entry regulation, I find no sorting effects. Thus, a contest with a relatively high prize does not necessarily attract contestants with higher abilities. Furthermore, I discover interesting spillover effects of prizes between contests in the case of restricted entry regulations. For instance, the individual (aggregate) effort increases (decreases) in a contest if the prize in another contest increases. The endogeneity of contestants’ participation drives many of these results.

A topic models based framework for detecting and forecasting emerging technologies

The identification of emerging technologies can bring valuable intelligence to enterprises and countries determining research and development (R&D) priorities. Emerging technologies are closely related to emerging topics in terms of several well-documented attributes: relatively fast growth, radical novelty and prominent impact. Our previous work on detecting and forecasting emerging topics is adapted to measure technology emergence, but the dynamic influence model (DIM) is replaced by the topical n-grams (TNG) model in this framework to nominate several emerging technologies in technical terms and to exploit the potential of topic models. Hence, technologies are viewed as term-based themes in this study. Three indicators are designed to reflect the above attributes: the fast growth indicator, the radical novelty indicator and the prominent impact indicator. The relatively fast growth indicator is calculated from the results of the TNG model and the radical novelty indicator comes from the citation influence model (CIM). As for the prominent impact indicator, the involving authors are used after name disambiguation and credit allocation. The following fields are utilized to develop the models: title, abstract, keywords-author, publication year, byline information, and cited references. We participated in the 2018–2019 Measuring Tech Emergence Contest with the proposed method, and 8 out of 10 submitted ones met the contest organizer’s criteria of technology emergence. Criteria included the percentage of high growth terms out of total terms provided, the degree of growth of the terms, and the frequency of those high growth terms across the dataset. Then, a qualitative assessment of overall methodology was conducted by three judges. In the end, we won Second Prize in the contest.

Information disclosure on the contest mechanism

In this paper, we study how to disclose the precision of the winner selection mechanism (i.e., the contest success function) that determines how effective a player’s effort is in determining his/her winning probability. Specifically, we focus on the disclosure of the adopted discriminatory power r of the Tullock contest. The discriminatory power r is exogenously given, and the contest organizer knows more than contestants about the true state of r. We examine the optimal disclosure policy that maximizes the expected sum of contestants’ efforts. In addition to public disclosure, we study private disclosure over groups with independent signals. We establish the optimality of full disclosure/concealment.

Liquidity constraints and the formation of unbalanced contests

We analyze the effects of future liquidity constraints on contestants’ investment in a dynamic contest model. Contestants invest in two consecutive contests to win a prize in each period. The loser of the first-period contest can be liquidity-constrained in the second period due to too little remaining wealth. The winner of the first contest can reinvest the prize in the second contest. We show that future liquidity constraints mainly affect the imbalance of the contest in the future but not today. Surprisingly, larger contest prizes decrease contestants’ future investments and amplify the imbalance of future contests. However, the contest organizer can reduce this imbalance by increasing the share of the prize money in the second contest.

Winner’s Optimal Reimbursement in Contest

This paper explores a designer-optimal reimbursement scheme in all-pay auctions with winner’s reimbursement. Assuming the reimbursement is a linear function of the cost of effort, we obtain analytical solutions for the contestants’ symmetrical equilibrium effort and the contest organizer’s expected revenue. We show that if the effort cost function is concave, the optimal reimbursement scheme is to return the full cost to the winner. On the contrary, if the effort cost function is convex, the optimal reimbursement scheme is not to compensate the winner. Moreover, we find that the organizer’s expected revenue may increase or decrease as the number of contestants increases when the winner is fully reimbursed.

Disclosure policies in all-pay auctions with bid caps and stochastic entry

This paper examines the effects of disclosing the actual number of bidders in contests with stochastic entry and with resource constraint. We study an all-pay auction with complete information. The auction entails one prize and n potential bidders. Each potential bidder has an exogenous probability of participation and faces an exogenous bid cap. It is shown that the contest organizer prefers fully concealing the information about the number of participating bidders. We extend the result to a case with endogenous entry.

Effects of risk aversion on all-pay auction with reimbursement

This paper investigates all-pay auctions with winner’s reimbursement and risk-averse contestants. Assuming the contestants share a general concave utility function, we obtain analytical solutions for the contestants’ symmetrical equilibrium effort and the contest organizer’s expected revenue. We show that both the equilibrium effort and expected revenue are lower when the contestants are risk averse than when they are risk neutral, and decrease in the degree of risk aversion. These results imply that with winner’s reimbursement, the contestant organizer prefers risk-neutral contestants to risk-reverse contestants and contrast with those obtained in the standard first-price sealed-bid auctions.

Disclosure Policies in All-pay Auctions with Affiliated Abilities

This paper investigates whether a contest organizer should disclose private information about bidders' abilities in an all-pay auction. Bidders' abilities are affiliated through an underlying state of the world and are accessible by the contest organizer. The organizer decides whether to disclose this information publicly. I find that the revenue ranking between full concealment and full revealing depends on the affiliation of bidders' abilities and the number of bidders. Full concealment renders a higher expected revenue if bidders' abilities are not too much affiliated or if there are two bidders. If the affiliation is too significant and there are more than two bidders, the revenue ranking between the two disclosure policies can hold in either direction. In particular, if the difference between low-ability and high-ability is not too much different and if either the chance to be of low-ability is sufficiently small in any state of the world or the number of bidders is sufficiently large, full disclosure dominates full concealment in terms of expected revenue. However, bidders strictly prefer full disclosure unless the states of the world are equivalent.

Disclosure Policies in All-pay Auctions with Bid Caps and Stochastic Entry

This paper contributes to the literature that examines the effects of disclosing the actual number of bidders in contests with stochastic entry by considering resource constraint. We study an all-pay auction with complete information. The auction entails one prize and $n$ potential bidders. Each potential bidder has an exogenous probability of participation and faces an exogenous bid cap. It is shown that the contest organizer prefers fully concealing the information about the number of participating bidders. We extend the result to a case with endogenous entry.

Information Disclosure in Contests: Private versus Public Signals

Two players compete for a prize in an all-pay auction where their private binary valuations are independent from each other. A contest organizer commits to disclose additional information about the opponent’s valuation to each player – privately or publicly – to maximize either players’ expected payoff or total expected effort. I characterize the unique equilibrium of the contest when the organizer discloses a public signal to all players and a symmetric equilibrium when he discloses a private signal to each. When the organizer discloses privately, I show that any partially informative private signals induce higher expected payoffs for players and lower total expected effort than when no signal is disclosed. When the organizer discloses publicly, I characterize a public disclosure policy that induces higher total expected effort than when no signal is disclosed. I also characterize optimal public signals that maximize players’ expected payoff. Finally, the ranking between private and public signals in terms of maximizing players’ expected payoff is indeterministic. In terms of revenue ranking, the all-pay auction with the public disclosure policy dominates the first- and the second-price auctions with binary independent private valuation regardless of whether private or public disclosure is used in these winner-pay auctions.

Semi-targeted all-pay auctions: A partial exclusion principle

This paper studies the effects of a specific affirmative action policy in complete information all-pay auctions when players differ in ability. The contest organizer splits the overall prize of the competition into a targeted and an untargeted prize. The targeted prize is exclusively for disadvantaged (low-ability) agents and excludes advantaged agents partially from the overall prize. We consider a setting with one high-ability and two low-ability contestants and fully characterize equilibrium. Assuming that the contest organizer aims to maximize expected total effort, we show that (i) almost any targeted prize is preferable to a standard all-pay auction without targeted prize; (ii) the exclusion principle Baye et al. (1993) can be implemented by a wide range of sufficiently large targeted prizes; and (iii) partial exclusion by means of an appropriately chosen targeted prize benefits the organizer more than complete exclusion. We also discuss the robustness of our results in settings with more than three agents.

RANKING DISCLOSURE POLICIES IN ALL‐PAY AUCTIONS

In this paper, we study information disclosure policies in all‐pay auctions with incomplete information. Two symmetric players have either high or low private value. The contest organizer observes players' values ex post, and can commit ex ante to four different symmetric information policies exhaustively: She can fully disclose or conceal the players' types, and she can disclose their types if and only if when both are high or if and only if when both are low. We characterize the unique equilibriums, and completely rank the four policies by various criteria. We find the full concealment policy extracts highest aggregate expected effort, followed by the policy of disclosing types if and only if when both are high. The policy of disclosing types if and only if when both are low induces least aggregate expected effort. Players enjoy highest expected payoffs under the policy of disclosing types if and only if when both are low; the other three policies yield the same expected payoffs to players. In terms of prize allocation efficiency, the full concealment policy is ranked highest and the full disclosure policy is ranked lowest. In between, the other two policies' rankings depend on the probability of high type. (JELC72, D44, D82, D83, J48).