When applying to schools and colleges, a key decision commonly faced by students is how to optimally choose their portfolio of schools. In many settings, large numbers of schools are available, but due to costs or constraints students apply only to a few, often without perfect information about how the school will respond to their application. Determining which subset of programs to apply to-balancing the desire to attend sought-after programs with the need to hedge-forms a critical part of the decision problem, with these decisions deeply affecting the final outcomes in the market. To achieve this balance, students are often advised to apply to a combination of "reach," "match," and "safety" schools [2]. In practice, it is also seen that when reductions in application costs permit students to apply to more schools, they expand the range of schools to which they apply both upwards and downwards, including safer and more selective schools [1, 7].
{"title":"Simultaneous Search: Beyond Independent Successes","authors":"Ran I. Shorrer","doi":"10.1145/3328526.3329599","DOIUrl":"https://doi.org/10.1145/3328526.3329599","url":null,"abstract":"When applying to schools and colleges, a key decision commonly faced by students is how to optimally choose their portfolio of schools. In many settings, large numbers of schools are available, but due to costs or constraints students apply only to a few, often without perfect information about how the school will respond to their application. Determining which subset of programs to apply to-balancing the desire to attend sought-after programs with the need to hedge-forms a critical part of the decision problem, with these decisions deeply affecting the final outcomes in the market. To achieve this balance, students are often advised to apply to a combination of \"reach,\" \"match,\" and \"safety\" schools [2]. In practice, it is also seen that when reductions in application costs permit students to apply to more schools, they expand the range of schools to which they apply both upwards and downwards, including safer and more selective schools [1, 7].","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132995081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. Despite having radically changed the way people get around in urban areas, there still remain a number of major challenges, undercutting their stated mission of "providing transportation as reliable as running water." A particular concern is that with the real-time flexibility to decide when and where to drive, drivers will strategize to improve their own income: calling riders to find out their destinations and canceling trips that are not worthwhile, declining trips and chasing surge prices in neighboring areas, and going off-line before large events end in anticipation of a price increase. Many of these incentive issues are a symptom of suboptimal dispatching, and a lack of smoothness in pricing in both time and space. For example, matching drivers to trips that sends them away from a sports stadium five minutes before a game ends, and at low prices, is inefficient, and drivers are responding to a suboptimal design, and may be acting to improve efficiency. In this paper, we study how to provide reliable and efficient transportation in the presence of spatial imbalances and temporal variations in supply and demand, while leaving drivers with the flexibility to decide how to work. We work in a complete information, discrete time, multi-period, multi-location model, and introduce the Spatio-Temporal Pricing (STP) mechanism. With information about supply and demand over a planning horizon, the STP mechanism solves for the welfare-optimal matching via a reduction to a minimum cost flow problem, and uses a connection between LP duality and market equilibrium to set prices that are smooth in both space and time. Without using penalties or time-extended contracts, the mechanism achieves incentive-alignment for drivers, in that it is a subgame-perfect equilibrium for drivers to always accept their trip dispatches. The mechanism is also robust to drivers' deviations, in that from any history onward, the equilibrium outcome under the mechanism is welfare-optimal, individually rational, budget balanced, core-selecting, and envy-free (drivers at the same location at the same time do not envy each other's downstream payoff). We also prove an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. An empirical analysis conducted in simulation suggests that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism.
{"title":"Spatio-Temporal Pricing for Ridesharing Platforms","authors":"Hongyao Ma, Fei Fang, David C. Parkes","doi":"10.1145/3328526.3329556","DOIUrl":"https://doi.org/10.1145/3328526.3329556","url":null,"abstract":"Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. Despite having radically changed the way people get around in urban areas, there still remain a number of major challenges, undercutting their stated mission of \"providing transportation as reliable as running water.\" A particular concern is that with the real-time flexibility to decide when and where to drive, drivers will strategize to improve their own income: calling riders to find out their destinations and canceling trips that are not worthwhile, declining trips and chasing surge prices in neighboring areas, and going off-line before large events end in anticipation of a price increase. Many of these incentive issues are a symptom of suboptimal dispatching, and a lack of smoothness in pricing in both time and space. For example, matching drivers to trips that sends them away from a sports stadium five minutes before a game ends, and at low prices, is inefficient, and drivers are responding to a suboptimal design, and may be acting to improve efficiency. In this paper, we study how to provide reliable and efficient transportation in the presence of spatial imbalances and temporal variations in supply and demand, while leaving drivers with the flexibility to decide how to work. We work in a complete information, discrete time, multi-period, multi-location model, and introduce the Spatio-Temporal Pricing (STP) mechanism. With information about supply and demand over a planning horizon, the STP mechanism solves for the welfare-optimal matching via a reduction to a minimum cost flow problem, and uses a connection between LP duality and market equilibrium to set prices that are smooth in both space and time. Without using penalties or time-extended contracts, the mechanism achieves incentive-alignment for drivers, in that it is a subgame-perfect equilibrium for drivers to always accept their trip dispatches. The mechanism is also robust to drivers' deviations, in that from any history onward, the equilibrium outcome under the mechanism is welfare-optimal, individually rational, budget balanced, core-selecting, and envy-free (drivers at the same location at the same time do not envy each other's downstream payoff). We also prove an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. An empirical analysis conducted in simulation suggests that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"12 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133895211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yang Cai, Nikhil R. Devanur, Kira Goldner, R. McAfee
We study a new model of complementary valuations, which we call "proportional complementarities.'' In contrast to common models, such as hypergraphic valuations, in our model, we do not assume that the extra value derived from owning a set of items is independent of the buyer's base valuations for the items. Instead, we model the complementarities as proportional to the buyer's base valuations, and these proportionalities are known market parameters. Our goal is to design a simple pricing scheme that, for a single buyer with proportional complementarities, yields approximately optimal revenue. We define a new class of mechanisms where some number of items are given away for free, and the remaining items are sold separately at inflated prices. We find that the better of such a mechanism and selling the grand bundle earns a 12-approximation to the optimal revenue for pairwise proportional complementarities. This confirms the intuition that items should not be sold completely separately in the presence of complementarities. In the more general case, a buyer has a maximum of proportional positive hypergraphic valuations, where a hyperedge in a given hypergraph describes the boost to the buyer's value for item i given by owning any set of items T in addition. The maximum-out-degree of such a hypergraph is d, and k is the positive rank of the hypergraph. For valuations given by these parameters, our simple pricing scheme is an O(min{d,k})-approximation.
{"title":"Simple and Approximately Optimal Pricing for Proportional Complementarities","authors":"Yang Cai, Nikhil R. Devanur, Kira Goldner, R. McAfee","doi":"10.1145/3328526.3329562","DOIUrl":"https://doi.org/10.1145/3328526.3329562","url":null,"abstract":"We study a new model of complementary valuations, which we call \"proportional complementarities.'' In contrast to common models, such as hypergraphic valuations, in our model, we do not assume that the extra value derived from owning a set of items is independent of the buyer's base valuations for the items. Instead, we model the complementarities as proportional to the buyer's base valuations, and these proportionalities are known market parameters. Our goal is to design a simple pricing scheme that, for a single buyer with proportional complementarities, yields approximately optimal revenue. We define a new class of mechanisms where some number of items are given away for free, and the remaining items are sold separately at inflated prices. We find that the better of such a mechanism and selling the grand bundle earns a 12-approximation to the optimal revenue for pairwise proportional complementarities. This confirms the intuition that items should not be sold completely separately in the presence of complementarities. In the more general case, a buyer has a maximum of proportional positive hypergraphic valuations, where a hyperedge in a given hypergraph describes the boost to the buyer's value for item i given by owning any set of items T in addition. The maximum-out-degree of such a hypergraph is d, and k is the positive rank of the hypergraph. For valuations given by these parameters, our simple pricing scheme is an O(min{d,k})-approximation.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129360925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the influence maximization problem in undirected networks, specifically focusing on the independent cascade and linear threshold models. We prove APX-hardness (NP-hardness of approximation within factor (1-τ) for some constant τ>0$) for both models, which improves the previous NP-hardness lower bound for the linear threshold model. No previous hardness result was known for the independent cascade model. As part of the hardness proof, we show some natural properties of these cascades on undirected graphs. For example, we show that the expected number of infections of a seed set S is upper-bounded by the size of the edge cut of S in the linear threshold model and a special case of the independent cascade model called the weighted independent cascade model. Motivated by our upper bounds, we present a suite of highly scalable local greedy heuristics for the influence maximization problem on both the linear threshold model and the weighted independent cascade model on undirected graphs that, in practice, find seed sets which on average obtain 97.52% of the performance of the much slower greedy algorithm for the linear threshold model, and 97.39% of the performance of the greedy algorithm for the weighted independent cascade model. Our heuristics also outperform other popular local heuristics, such as the degree discount heuristic by Chen et al.
{"title":"Influence Maximization on Undirected Graphs: Towards Closing the (1-1/e) Gap","authors":"G. Schoenebeck, Biaoshuai Tao","doi":"10.1145/3328526.3329650","DOIUrl":"https://doi.org/10.1145/3328526.3329650","url":null,"abstract":"We study the influence maximization problem in undirected networks, specifically focusing on the independent cascade and linear threshold models. We prove APX-hardness (NP-hardness of approximation within factor (1-τ) for some constant τ>0$) for both models, which improves the previous NP-hardness lower bound for the linear threshold model. No previous hardness result was known for the independent cascade model. As part of the hardness proof, we show some natural properties of these cascades on undirected graphs. For example, we show that the expected number of infections of a seed set S is upper-bounded by the size of the edge cut of S in the linear threshold model and a special case of the independent cascade model called the weighted independent cascade model. Motivated by our upper bounds, we present a suite of highly scalable local greedy heuristics for the influence maximization problem on both the linear threshold model and the weighted independent cascade model on undirected graphs that, in practice, find seed sets which on average obtain 97.52% of the performance of the much slower greedy algorithm for the linear threshold model, and 97.39% of the performance of the greedy algorithm for the weighted independent cascade model. Our heuristics also outperform other popular local heuristics, such as the degree discount heuristic by Chen et al.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128993407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep reinforcement learning (RL) is a powerful method for generating policies in complex environments, and recent breakthroughs in game-playing have leveraged deep RL as part of an iterative multiagent search process. We build on such developments and present an approach that learns progressively better mixed strategies in complex dynamic games of imperfect information, through iterated use of empirical game-theoretic analysis (EGTA) with deep RL policies. We apply the approach to a challenging cybersecurity game defined over attack graphs. Iterating deep RL with EGTA to convergence over dozens of rounds, we generate mixed strategies far stronger than earlier published heuristic strategies for this game. We further refine the strategy-exploration process, by fine-tuning in a training environment that includes out-of-equilibrium but recently seen opponents. Experiments suggest this history-aware approach yields strategies with lower regret at each stage of training.
{"title":"Iterated Deep Reinforcement Learning in Games: History-Aware Training for Improved Stability","authors":"Mason Wright, Yongzhao Wang, Michael P. Wellman","doi":"10.1145/3328526.3329634","DOIUrl":"https://doi.org/10.1145/3328526.3329634","url":null,"abstract":"Deep reinforcement learning (RL) is a powerful method for generating policies in complex environments, and recent breakthroughs in game-playing have leveraged deep RL as part of an iterative multiagent search process. We build on such developments and present an approach that learns progressively better mixed strategies in complex dynamic games of imperfect information, through iterated use of empirical game-theoretic analysis (EGTA) with deep RL policies. We apply the approach to a challenging cybersecurity game defined over attack graphs. Iterating deep RL with EGTA to convergence over dozens of rounds, we generate mixed strategies far stronger than earlier published heuristic strategies for this game. We further refine the strategy-exploration process, by fine-tuning in a training environment that includes out-of-equilibrium but recently seen opponents. Experiments suggest this history-aware approach yields strategies with lower regret at each stage of training.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126708377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study a classical Bayesian mechanism design problem where a seller is selling multiple items to a buyer. We consider the case where the seller has costs to produce the items, and these costs are private information to the seller. How can the seller design a mechanism to maximize her profit? Two well-studied problems, revenue maximization in multi-item auctions and signaling in ad auctions, are special cases of our problem. We show that there exists a simple mechanism whose profit is at least 1/11 the optimal profit, when the buyer has a constraint-additive valuation over independent items. The approximation factor becomes 6 when the buyer is additive. Our result holds even when the seller's costs are correlated across items. We introduce a new class of mechanisms called permit-selling mechanisms. These mechanisms have two stages. For each item i, we create a separate permit that allows the buyer to purchase the item at its cost. In the first stage, we sell the permits without revealing any information about the costs. In the second stage, the seller reveals all the costs, and the buyer can buy item i by only paying the cost $c_i$ if the buyer has purchased the permit for item i in the first stage. We show that the best permit-selling mechanism or the best posted price mechanism is already a constant factor approximation to the optimal profit (6 for additive, and 11 for constrained additive). Indeed, we do not require the optimal permit-selling mechanism, only selling the permits separately or as a grand bundle suffices to achieve the above approximation ratio. Our proof is enabled by constructing a benchmark for the optimal profit via a novel dual solution and a new connection to revenue maximization in multi-item auctions with a subadditive bidder.
{"title":"Simple Mechanisms for Profit Maximization in Multi-item Auctions","authors":"Yang Cai, Mingfei Zhao","doi":"10.1145/3328526.3329616","DOIUrl":"https://doi.org/10.1145/3328526.3329616","url":null,"abstract":"We study a classical Bayesian mechanism design problem where a seller is selling multiple items to a buyer. We consider the case where the seller has costs to produce the items, and these costs are private information to the seller. How can the seller design a mechanism to maximize her profit? Two well-studied problems, revenue maximization in multi-item auctions and signaling in ad auctions, are special cases of our problem. We show that there exists a simple mechanism whose profit is at least 1/11 the optimal profit, when the buyer has a constraint-additive valuation over independent items. The approximation factor becomes 6 when the buyer is additive. Our result holds even when the seller's costs are correlated across items. We introduce a new class of mechanisms called permit-selling mechanisms. These mechanisms have two stages. For each item i, we create a separate permit that allows the buyer to purchase the item at its cost. In the first stage, we sell the permits without revealing any information about the costs. In the second stage, the seller reveals all the costs, and the buyer can buy item i by only paying the cost $c_i$ if the buyer has purchased the permit for item i in the first stage. We show that the best permit-selling mechanism or the best posted price mechanism is already a constant factor approximation to the optimal profit (6 for additive, and 11 for constrained additive). Indeed, we do not require the optimal permit-selling mechanism, only selling the permits separately or as a grand bundle suffices to achieve the above approximation ratio. Our proof is enabled by constructing a benchmark for the optimal profit via a novel dual solution and a new connection to revenue maximization in multi-item auctions with a subadditive bidder.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129079530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The spread betting market is a prevalent form of prediction market. In the spread betting market, participants bet on the outcome of a certain future event. The market maker quotes cutoff lines as "prices," and bettors take sides on whether the event outcome exceeds the quoted spread lines. We study how the market maker should move the spread lines to maximize profit. In our model, anonymous bettors with heterogeneous strategic behavior and information levels participate in the market. The market maker has limited information on the event outcome distribution. She aims to extract information from the market's responses to her spread lines (i.e., "learning") while guarding against an informed bettor's strategic manipulation (i.e., "bluff-proofing"). In terms of effective policies to adjust the market maker's spread lines, we show that Bayesian policies (BPs) that ignore bluffing are typically vulnerable to the informed bettor's strategic manipulation. To be more precise, the regret for the market maker is linear in the number of bets, and we identify certain strategies of the informed bettor that are profitable. We also show that the poor performance of BPs in our setting is not due to incomplete learning: when the informed bettor is absent in our setting, many simple policies eventually learn the event outcome distribution and achieve a bounded regret. Full Paper: https://ssrn.com/abstract=3283392
{"title":"Dynamic Learning and Market Making in Spread Betting Markets with Informed Bettors","authors":"J. Birge, Yifan Feng, N. B. Keskin, Adam Schultz","doi":"10.1145/3328526.3329646","DOIUrl":"https://doi.org/10.1145/3328526.3329646","url":null,"abstract":"The spread betting market is a prevalent form of prediction market. In the spread betting market, participants bet on the outcome of a certain future event. The market maker quotes cutoff lines as \"prices,\" and bettors take sides on whether the event outcome exceeds the quoted spread lines. We study how the market maker should move the spread lines to maximize profit. In our model, anonymous bettors with heterogeneous strategic behavior and information levels participate in the market. The market maker has limited information on the event outcome distribution. She aims to extract information from the market's responses to her spread lines (i.e., \"learning\") while guarding against an informed bettor's strategic manipulation (i.e., \"bluff-proofing\"). In terms of effective policies to adjust the market maker's spread lines, we show that Bayesian policies (BPs) that ignore bluffing are typically vulnerable to the informed bettor's strategic manipulation. To be more precise, the regret for the market maker is linear in the number of bets, and we identify certain strategies of the informed bettor that are profitable. We also show that the poor performance of BPs in our setting is not due to incomplete learning: when the informed bettor is absent in our setting, many simple policies eventually learn the event outcome distribution and achieve a bounded regret. Full Paper: https://ssrn.com/abstract=3283392","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121975919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hao Yan, Sanmay Das, Allen Lavoie, Sirui Li, Betsy Sinclair
In this paper, we study the effectiveness and generalizability of techniques for classifying partisanship and ideology from text in the context of US politics. In particular, we are interested in how well measures of partisanship transfer across domains as well as the potential to rely upon measures of partisan intensity as a proxy for political ideology. We construct novel datasets of English texts from (1) the Congressional Record, (2) prominent conservative and liberal media websites, and (3) conservative and liberal wikis, and apply text classification algorithms to evaluate domain specificity via a domain adaptation technique. Surprisingly, we find that the cross-domain learning performance, benchmarking the ability to generalize from one of these datasets to another, is in general poor, even though the algorithms perform very well in within-dataset cross-validation tests. While party affiliation of legislators is not predictable based on models learned from other sources, we do find some ability to predict the leanings of the media and crowdsourced websites based on models learned from the Congressional Record. This predictivity is different across topics, and itself a priori predictable based on within-topic cross-validation results. Temporally, phrases tend to move from politicians to the media, helping to explain this predictivity. Finally, when we compare legislators themselves across different media (the Congressional Record and press releases), we find that while party affiliation is highly predictable, within-party ideology is completely unpredictable. Legislators are communicating different messages through different channels while clearly signaling party identity systematically across all channels. Choice of language is a clearly strategic act, among both legislators and the media, and we must therefore proceed with extreme caution in extrapolating from language to partisanship or ideology across domains.
{"title":"The Congressional Classification Challenge: Domain Specificity and Partisan Intensity","authors":"Hao Yan, Sanmay Das, Allen Lavoie, Sirui Li, Betsy Sinclair","doi":"10.1145/3328526.3329582","DOIUrl":"https://doi.org/10.1145/3328526.3329582","url":null,"abstract":"In this paper, we study the effectiveness and generalizability of techniques for classifying partisanship and ideology from text in the context of US politics. In particular, we are interested in how well measures of partisanship transfer across domains as well as the potential to rely upon measures of partisan intensity as a proxy for political ideology. We construct novel datasets of English texts from (1) the Congressional Record, (2) prominent conservative and liberal media websites, and (3) conservative and liberal wikis, and apply text classification algorithms to evaluate domain specificity via a domain adaptation technique. Surprisingly, we find that the cross-domain learning performance, benchmarking the ability to generalize from one of these datasets to another, is in general poor, even though the algorithms perform very well in within-dataset cross-validation tests. While party affiliation of legislators is not predictable based on models learned from other sources, we do find some ability to predict the leanings of the media and crowdsourced websites based on models learned from the Congressional Record. This predictivity is different across topics, and itself a priori predictable based on within-topic cross-validation results. Temporally, phrases tend to move from politicians to the media, helping to explain this predictivity. Finally, when we compare legislators themselves across different media (the Congressional Record and press releases), we find that while party affiliation is highly predictable, within-party ideology is completely unpredictable. Legislators are communicating different messages through different channels while clearly signaling party identity systematically across all channels. Choice of language is a clearly strategic act, among both legislators and the media, and we must therefore proceed with extreme caution in extrapolating from language to partisanship or ideology across domains.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132392819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The computation of market equilibria is a fundamental and practically relevant research question. Advances in computational optimization allow for the organization of large combinatorial markets in the field nowadays. While we know the computational complexity and the types of price functions necessary on combinatorial exchanges with quasi-linear preferences, prior literature did not consider financially constrained buyers. We aim at allocations and competitive equilibrium prices that respect budget constraints. Such constraints are an important concern for the design of real-world markets, but we show that the allocation and pricing problem becomes even Σ2p-hard. Problems in this complexity class are rare, but ignoring budget constraints can lead to significant efficiency losses and instability. We introduce mixed integer bilevel linear programs (MIBLP) to compute core prices, and effective column and constraint generation algorithms to solve the problems. While full core stability becomes quickly intractable, we show that small but realistic problem sizes can actually be solved if the designer limits attention to deviations of small coalitions. This n-coalition stability is a practical approach to tame the computational complexity of the general problem and at the same time provide a reasonable level of stability.
{"title":"Computing Core-Stable Outcomes in Combinatorial Exchanges with Financially Constrained Bidders","authors":"M. Bichler, S. Waldherr","doi":"10.1145/3328526.3329641","DOIUrl":"https://doi.org/10.1145/3328526.3329641","url":null,"abstract":"The computation of market equilibria is a fundamental and practically relevant research question. Advances in computational optimization allow for the organization of large combinatorial markets in the field nowadays. While we know the computational complexity and the types of price functions necessary on combinatorial exchanges with quasi-linear preferences, prior literature did not consider financially constrained buyers. We aim at allocations and competitive equilibrium prices that respect budget constraints. Such constraints are an important concern for the design of real-world markets, but we show that the allocation and pricing problem becomes even Σ2p-hard. Problems in this complexity class are rare, but ignoring budget constraints can lead to significant efficiency losses and instability. We introduce mixed integer bilevel linear programs (MIBLP) to compute core prices, and effective column and constraint generation algorithms to solve the problems. While full core stability becomes quickly intractable, we show that small but realistic problem sizes can actually be solved if the designer limits attention to deviations of small coalitions. This n-coalition stability is a practical approach to tame the computational complexity of the general problem and at the same time provide a reasonable level of stability.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"48 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113974219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sortition is an alternative approach to democracy, in which representatives are not elected but randomly selected from the population. Most electoral democracies fail to accurately represent even a handful of protected groups. By contrast, sortition guarantees that every subset of the population will in expectation fill their fair share of the available positions. This fairness property remains satisfied when the sample is stratified based on known features. Moreover, stratification can greatly reduce the variance in the number of positions filled by any unknown group, as long as this group correlates with the strata. Our main result is that stratification cannot increase this variance by more than a negligible factor, even in the presence of indivisibilities and rounding. When the unknown group is unevenly spread across strata, we give a guarantee on the reduction in variance with respect to uniform sampling. We also contextualize stratification and uniform sampling in the space of fair sampling algorithms. Finally, we apply our insights to an empirical case study.
{"title":"No Stratification Without Representation","authors":"Gerdus Benade, Paul Gölz, A. Procaccia","doi":"10.1145/3328526.3329578","DOIUrl":"https://doi.org/10.1145/3328526.3329578","url":null,"abstract":"Sortition is an alternative approach to democracy, in which representatives are not elected but randomly selected from the population. Most electoral democracies fail to accurately represent even a handful of protected groups. By contrast, sortition guarantees that every subset of the population will in expectation fill their fair share of the available positions. This fairness property remains satisfied when the sample is stratified based on known features. Moreover, stratification can greatly reduce the variance in the number of positions filled by any unknown group, as long as this group correlates with the strata. Our main result is that stratification cannot increase this variance by more than a negligible factor, even in the presence of indivisibilities and rounding. When the unknown group is unevenly spread across strata, we give a guarantee on the reduction in variance with respect to uniform sampling. We also contextualize stratification and uniform sampling in the space of fair sampling algorithms. Finally, we apply our insights to an empirical case study.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114041220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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