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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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	<rdf:li rdf:resource="https://www.berggruen.org/ideas/articles/the-mutualist-economy-a-new-deal-for-ownership/"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1204.4286"/>
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  </channel><item rdf:about="https://arxiv.org/abs/2103.14000">
    <title>[2103.14000] Fairness in Ranking: A Survey</title>
    <dc:date>2022-04-19T10:28:14+00:00</dc:date>
    <link>https://arxiv.org/abs/2103.14000</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs.
]]></description>
<dc:subject>machine-learning fairness rather-interesting ethics it's-more-complicated-than-you-think</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e96c4423254a/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ethics"/>
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<item rdf:about="https://arxiv.org/abs/1806.02711">
    <title>[1806.02711] POTs: Protective Optimization Technologies</title>
    <dc:date>2020-10-13T20:46:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1806.02711</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems. 
We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial. 
We propose Protective Optimization Technologies (POTs). POTs provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic-beating applications, and recalibrating credit scoring for loan applicants.
]]></description>
<dc:subject>fairness algorithms coevolution-of-technocracy rather-interesting social-dynamics to-write-about consider:hey-maybe-don't-do-that-thing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:41240cd935db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
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<item rdf:about="https://www.berggruen.org/ideas/articles/the-mutualist-economy-a-new-deal-for-ownership/">
    <title>The Mutualist Economy: A New Deal for Ownership - Ideas - Berggruen Institute</title>
    <dc:date>2020-05-04T11:58:56+00:00</dc:date>
    <link>https://www.berggruen.org/ideas/articles/the-mutualist-economy-a-new-deal-for-ownership/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This essay proposes a new model of personal and public wealth-building that can address the current crisis of inequality in the United States. We place contemporary American wealth inequality into its historical context by tracing how federal government policies have worked to support personal and public wealth building across three periods: the First Industrial Revolution of the mid-19th century, the Second Industrial Revolution of the early 20th century, and the Information and Communication Technology revolution of the late-20th century. We then suggest a series of potential governmental policies that can help to ensure a more equitable wealth distribution in the future. Our proposed “mutualist” model of political economy would allow for the large-scale diffusion of productivity gains that may follow the installation of deployment of the next wave of general-purpose technologies. This new social contract will move beyond the welfare state’s focus on insurance toward a more radical notion of shared ownership of returns on capital via universal individual capital endowments and new public investment channels that control shares in firms and intellectual property.

]]></description>
<dc:subject>political-economy inequality fairness economics cultural-norms finance to-read via:several post-normal-society</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2c82699444bd/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1911.09792">
    <title>[1911.09792] Minority Voter Distributions and Partisan Gerrymandering</title>
    <dc:date>2020-01-19T18:38:45+00:00</dc:date>
    <link>https://arxiv.org/abs/1911.09792</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Many people believe that it is disadvantageous for members aligning with a minority party to cluster in cities, as this makes it easier for the majority party to gerrymander district boundaries to diminish the representation of the minority. We examine this effect by exhaustively computing the average representation for every possible 5×5 grid of population placement and district boundaries. We show that, in fact, it is advantageous for the minority to arrange themselves in clusters, as it is positively correlated with representation. We extend this result to more general cases by considering the dual graph of districts, and we also propose and analyze metaheuristic algorithms that allow us to find strong lower bounds for maximum expected representation.]]></description>
<dc:subject>gerrymandering voting looking-to-see simulation rather-interesting fairness optimization multiobjective-optimization to-simulate to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:93dfb88e453c/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1905.10546">
    <title>[1905.10546] Protecting the Protected Group: Circumventing Harmful Fairness</title>
    <dc:date>2020-01-01T14:14:49+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.10546</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Machine Learning (ML) algorithms shape our lives. Banks use them to determine if we are good borrowers; IT companies delegate them recruitment decisions; police apply ML for crime-prediction, and judges base their verdicts on ML. However, real-world examples show that such automated decisions tend to discriminate against protected groups. This potential discrimination generated a huge hype both in media and in the research community. Quite a few formal notions of fairness were proposed, which take a form of constraints a "fair" algorithm must satisfy. We focus on scenarios where fairness is imposed on a self-interested party (e.g., a bank that maximizes its revenue). We find that the disadvantaged protected group can be worse off after imposing a fairness constraint. We introduce a family of \textit{Welfare-Equalizing} fairness constraints that equalize per-capita welfare of protected groups, and include \textit{Demographic Parity} and \textit{Equal Opportunity} as particular cases. In this family, we characterize conditions under which the fairness constraint helps the disadvantaged group. We also characterize the structure of the optimal \textit{Welfare-Equalizing} classifier for the self-interested party, and provide an algorithm to compute it. Overall, our \textit{Welfare-Equalizing} fairness approach provides a unified framework for discussing fairness in classification in the presence of a self-interested party.
]]></description>
<dc:subject>fairness multiobjective-optimization algorithms machine-learning technocracy rather-interesting the-mangle-in-practice to-write-about consider:not-using-the-same-framework-for-all-your-goals</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:38233e82c5cf/</dc:identifier>
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<item rdf:about="https://feministkilljoys.com/2017/08/09/a-complaint-biography/">
    <title>A Complaint Biography | feministkilljoys</title>
    <dc:date>2019-09-23T15:08:42+00:00</dc:date>
    <link>https://feministkilljoys.com/2017/08/09/a-complaint-biography/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It is also the case that statements that are not intended as complaints can be received as complaints. Just using words such as racism or sexism can mean being heard as making a complaint. If we think of the word complaint we might think of a formal statement; a complaint as something you officially lodge. But if we think of the word “complaining” it brings up something else; it brings up somebody else. The word complaining has a negative quality: the word belongs with the killjoy in the same family of words; complaining, killjoy, whinging, moaning, buzzkill, party-pooper; stick-in-the-mud. In an earlier post, I described how being heard as complaining is not being heard. You are heard as expressing yourself; as if you are complaining because that is who you are or what you are like. If you are heard as complaining then what you say is dismissible, as if you are complaining because that is your personal tendency. When you are heard as complaining you lose the about: what you are speaking about is not heard when they make it about you.

]]></description>
<dc:subject>cultural-norms fairness racism diversity bureaucracy looking-to-see power-relations abuse-of-power reputation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c4b841be6098/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1705.10239">
    <title>[1705.10239] Fair Division of a Graph</title>
    <dc:date>2019-03-29T12:24:08+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.10239</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider fair allocation of indivisible items under an additional constraint: there is an undirected graph describing the relationship between the items, and each agent's share must form a connected subgraph of this graph. This framework captures, e.g., fair allocation of land plots, where the graph describes the accessibility relation among the plots. We focus on agents that have additive utilities for the items, and consider several common fair division solution concepts, such as proportionality, envy-freeness and maximin share guarantee. While finding good allocations according to these solution concepts is computationally hard in general, we design efficient algorithms for special cases where the underlying graph has simple structure, and/or the number of agents -or, less restrictively, the number of agent types- is small. In particular, despite non-existence results in the general case, we prove that for acyclic graphs a maximin share allocation always exists and can be found efficiently.
]]></description>
<dc:subject>assignment-problems fairness operations-research planning collective-behavior cake-cutting game-theory constraint-satisfaction rather-interesting variant-problems mathematical-recreations to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1ba1587e85a7/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1507.06827">
    <title>[1507.06827] Egalitarianism of Random Assignment Mechanisms</title>
    <dc:date>2019-03-08T18:23:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1507.06827</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the egalitarian welfare aspects of random assignment mechanisms when agents have unrestricted cardinal utilities over the objects. We give bounds on how well different random assignment mechanisms approximate the optimal egalitarian value and investigate the effect that different well-known properties like ordinality, envy-freeness, and truthfulness have on the achievable egalitarian value. Finally, we conduct detailed experiments analyzing the tradeoffs between efficiency with envy-freeness or truthfulness using two prominent random assignment mechanisms --- random serial dictatorship and the probabilistic serial mechanism --- for different classes of utility functions and distributions.]]></description>
<dc:subject>game-theory assignment-problems fairness collective-behavior mechanism-design to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:df9322fa9c4a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
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<item rdf:about="https://arxiv.org/abs/1705.09444">
    <title>[1705.09444] Equilibria in Sequential Allocation</title>
    <dc:date>2019-03-08T01:17:21+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.09444</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Sequential allocation is a simple mechanism for sharing multiple indivisible items. We study strategic behavior in sequential allocation. In particular, we consider Nash dynamics, as well as the computation and Pareto optimality of pure equilibria, and Stackelberg strategies. We first demonstrate that, even for two agents, better responses can cycle. We then present a linear-time algorithm that returns a profile (which we call the "bluff profile") that is in pure Nash equilibrium. Interestingly, the outcome of the bluff profile is the same as that of the truthful profile and the profile is in pure Nash equilibrium for \emph{all} cardinal utilities consistent with the ordinal preferences. We show that the outcome of the bluff profile is Pareto optimal with respect to pairwise comparisons. In contrast, we show that an assignment may not be Pareto optimal with respect to pairwise comparisons even if it is a result of a preference profile that is in pure Nash equilibrium for all utilities consistent with ordinal preferences. Finally, we present a dynamic program to compute an optimal Stackelberg strategy for two agents, where the second agent has a constant number of distinct values for the items.
]]></description>
<dc:subject>game-theory allocation-problems planning algorithms fairness performance-measure rather-interesting to-simulate consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:96a59cea4697/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
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<item rdf:about="https://arxiv.org/abs/1602.06940">
    <title>[1602.06940] Complexity of Manipulating Sequential Allocation</title>
    <dc:date>2019-03-08T01:16:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1602.06940</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Sequential allocation is a simple allocation mechanism in which agents are given pre-specified turns and each agents gets the most preferred item that is still available. It has long been known that sequential allocation is not strategyproof. 
Bouveret and Lang (2014) presented a polynomial-time algorithm to compute a best response of an agent with respect to additively separable utilities and claimed that (1) their algorithm correctly finds a best response, and (2) each best response results in the same allocation for the manipulator. We show that both claims are false via an example. We then show that in fact the problem of computing a best response is NP-complete. On the other hand, the insights and results of Bouveret and Lang (2014) for the case of two agents still hold.]]></description>
<dc:subject>game-theory assignment-problems rather-interesting algorithms planning fairness nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:87283812c4ed/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1503.06665">
    <title>[1503.06665] The Adjusted Winner Procedure: Characterizations and Equilibria</title>
    <dc:date>2017-03-20T11:22:15+00:00</dc:date>
    <link>https://arxiv.org/abs/1503.06665</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The Adjusted Winner procedure is an important fair division mechanism proposed by Brams and Taylor for allocating goods between two parties. It has been used in practice for divorce settlements and analyzing political disputes. Assuming truthful declaration of the valuations, it computes an allocation that is envy-free, equitable and Pareto optimal. 
We show that Adjusted Winner admits several elegant characterizations, which further shed light on the outcomes reached with strategic agents. We find that the procedure may not admit pure Nash equilibria in either the discrete or continuous variants, but is guaranteed to have ϵ-Nash equilibria for each ϵ > 0. Moreover, under informed tie-breaking, exact pure Nash equilibria always exist, are Pareto optimal, and their social welfare is at least 3/4 of the optimal.
]]></description>
<dc:subject>game-theory fairness algorithms mechanism-design rather-interesting to-write-about consider:looking-to-see consider:agents</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:28507e414fa6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanism-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:agents"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1608.02014">
    <title>[1608.02014] Assessing significance in a Markov chain without mixing</title>
    <dc:date>2017-02-19T12:07:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1608.02014</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a new statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to demonstrate rigorously that the presented state is an outlier with respect to the values, by establishing a p-value for observations we make about the state under the null hypothesis that it was chosen uniformly at random. 
A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain, and compare these to the rank of the presented state; if the presented state is a 0.1%-outlier compared to the sampled ranks (i.e., its rank is in the bottom 0.1% of sampled ranks) then this should correspond to a p-value of 0.001. This test is not rigorous, however, without good bounds on the mixing time of the Markov chain, as one must argue that the observed states on the trajectory approximate the stationary distribution. 
Our test is the following: given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an ε-outlier on the walk is significant at p=2ε‾‾√, under the null hypothesis that the state was chosen from a stationary distribution. Our result assumes nothing about the structure of the Markov chain beyond reversibility, and we construct examples to show that significance at p≈ε√ is essentially best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districtings.
]]></description>
<dc:subject>statistics Markov-chains random-walks stochastic-systems rather-interesting statistical-test algorithms to-write-about politics redistricting fairness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fd35bb1fbbb1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Markov-chains"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:random-walks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stochastic-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistical-test"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:redistricting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1307.2225">
    <title>[1307.2225] An Algorithmic Framework for Strategic Fair Division</title>
    <dc:date>2016-12-25T22:09:30+00:00</dc:date>
    <link>https://arxiv.org/abs/1307.2225</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the paradigmatic fair division problem of allocating a divisible good among agents with heterogeneous preferences, commonly known as cake cutting. Classical cake cutting protocols are susceptible to manipulation. Do their strategic outcomes still guarantee fairness? 
To address this question we adopt a novel algorithmic approach, by designing a concrete computational framework for fair division---the class of Generalized Cut and Choose (GCC) protocols}---and reasoning about the game-theoretic properties of algorithms that operate in this model. The class of GCC protocols includes the most important discrete cake cutting protocols, and turns out to be compatible with the study of fair division among strategic agents. In particular, GCC protocols are guaranteed to have approximate subgame perfect Nash equilibria, or even exact equilibria if the protocol's tie-breaking rule is flexible. We further observe that the (approximate) equilibria of proportional GCC protocols---which guarantee each of the n agents a 1/n-fraction of the cake---must be (approximately) proportional. Finally, we design a protocol in this framework with the property that its Nash equilibrium allocations coincide with the set of (contiguous) envy-free allocations.
]]></description>
<dc:subject>game-theory cake-cutting mathematical-recreations fairness agent-based planning nudge-targets consider:looking-to-see consider:representation Winkler-project to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ee298940e2ce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cake-cutting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mathematical-recreations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Winkler-project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1609.07236">
    <title>[1609.07236] On the (im)possibility of fairness</title>
    <dc:date>2016-10-16T11:46:12+00:00</dc:date>
    <link>https://arxiv.org/abs/1609.07236</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[What does it mean for an algorithm to be fair? Different papers use different notions of algorithmic fairness, and although these appear internally consistent, they also seem mutually incompatible. We present a mathematical setting in which the distinctions in previous papers can be made formal. In addition to characterizing the spaces of inputs (the "observed" space) and outputs (the "decision" space), we introduce the notion of a construct space: a space that captures unobservable, but meaningful variables for the prediction. 
We show that in order to prove desirable properties of the entire decision-making process, different mechanisms for fairness require different assumptions about the nature of the mapping from construct space to decision space. The results in this paper imply that future treatments of algorithmic fairness should more explicitly state assumptions about the relationship between constructs and observations.
]]></description>
<dc:subject>algorithms on-beyond-Arrow machine-learning fairness philosophy-of-engineering rather-interesting to-write-about via:cshalizi</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a46d1bc735ed/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:on-beyond-Arrow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1409.6765">
    <title>[1409.6765] A Generalization of the AL method for Fair Allocation of Indivisible Objects</title>
    <dc:date>2014-10-25T13:06:52+00:00</dc:date>
    <link>http://arxiv.org/abs/1409.6765</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the assignment problem in which agents express ordinal preferences over m objects and the objects are allocated to the agents based on the preferences.In a recent paper, Brams, Kilgour, and Klamler (2014) presented the AL method to compute an envy-free assignment for two agents. The AL method crucially depends on the assumption that agents have strict preferences over objects. We generalize the AL method to the case where agents may express indifferences and prove the axiomatic properties satisfied by the algorithm. As a result of the generalization, we also get a O(m) speedup on previous algorithms to check whether a complete envy-free assignment exists or not.
]]></description>
<dc:subject>game-theory economics fairness algorithms computational-complexity nudge-targets clearly</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f33ab7770a1a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clearly"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.0803">
    <title>[1311.0803] When &quot;I cut, you choose&quot; method implies intransitivity</title>
    <dc:date>2014-03-31T21:31:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.0803</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[There is common belief that humans and many animals follow transitive inference (choosing A over C on the basis of knowing that A is better than B and B is better than C). Transitivity seems to be the essence of rational choice. We present a theoretical model of the repeated game in which the players make a choice between the three goods (e.g. food). Rules of the game refer to a simple procedure of fair division among two players, known as the "I cut, you choose" mechanism widely discussed in literature. In this game one of the players has to make intransitive choices to achieve optimal result (for him and his co-player). The point is that intransitive choice can be rational.
]]></description>
<dc:subject>game-theory fairness edge-cases interesting nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:54fda4db7258/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:edge-cases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1307.2908">
    <title>[1307.2908] Cake Cutting Algorithms for Piecewise Constant and Piecewise Uniform Valuations</title>
    <dc:date>2013-11-03T12:50:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.2908</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Cake cutting is one of the most fundamental settings in fair division and mechanism design without money. In this paper, we consider different levels of three fundamental goals in cake cutting: fairness, Pareto optimality, and strategyproofness. In particular, we present robust versions of envy-freeness and proportionality that are not only stronger than their standard counter-parts but also have less information requirements. We then focus on cake cutting with piecewise constant valuations and present three desirable algorithms: CCEA (Controlled Cake Eating Algorithm), MEA (Market Equilibrium Algorithm) and CSD (Constrained Serial Dictatorship). CCEA is polynomial-time, robust envy-free, and non-wasteful. It relies on parametric network flows and recent generalizations of the probabilistic serial algorithm. For the subdomain of piecewise uniform valuations, we show that it is also group-strategyproof. Then, we show that there exists an algorithm (MEA) that is polynomial-time, envy-free, proportional, and Pareto optimal. MEA is based on computing a market-based equilibrium via a convex program and relies on the results of Reijnierse and Potters [24] and Devanur et al. [15]. Moreover, we show that MEA and CCEA are equivalent to mechanism 1 of Chen et. al. [12] for piecewise uniform valuations. We then present an algorithm CSD and a way to implement it via randomization that satisfies strategyproofness in expectation, robust proportionality, and unanimity for piecewise constant valuations. For the case of two agents, it is robust envy-free, robust proportional, strategyproof, and polynomial-time. Many of our results extend to more general settings in cake cutting that allow for variable claims and initial endowments. We also show a few impossibility results to complement our algorithms.
]]></description>
<dc:subject>cake-cutting algorithms fairness stamp-collecting nudge-targets mechanism-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:67c4f3906bdc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cake-cutting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stamp-collecting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanism-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.0022">
    <title>[1212.0022] Mathematical Frameworks for Pricing in the Cloud: Revenue, Fairness, and Resource Allocations</title>
    <dc:date>2013-03-07T11:27:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.0022</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As more and more users begin to use the cloud for their computing needs, datacenter operators are increasingly pressed to effectively allocate their resources among these client users. Yet while much work has been done in this area, relatively little attention has been paid to studying perhaps the ultimate lever of resource allocation: pricing. Most data centers today charge users by "bundling" heterogeneous resources together in a fixed ratio and selling these bundles to their clients. But bundling masks the fact that different users require different combinations of resources (e.g., CPUs, memory, bandwidth) to process their jobs. The presence of multiple resources in fact allows an operator to offer many different types of pricing strategies, which may have different effects on its revenue. Moreover, to avoid user dissatisfaction, operators must consider the impact of their chosen prices on the fairness of the jobs processed for different users. In this paper, we develop an analytical framework that accounts for the fairness and revenue tradeoffs that arise in a datacenter's multi-resource setting and the impact that different pricing plans can have on this tradeoff. We characterize the implications of different pricing plans on various fairness metrics and derive analytical limits on the operator's fairness-revenue tradeoff. We then provide an algorithm to navigate this tradeoff and compare the tradeoff points for different pricing strategies on a data trace taken from a Google cluster.]]></description>
<dc:subject>economics pricing fairness multiobjective-optimization algorithms nudge-targets cloud-computing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:42f494bf93da/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pricing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cloud-computing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1204.4286">
    <title>[1204.4286] Fair Allocation Without Trade</title>
    <dc:date>2012-04-21T12:49:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.4286</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We consider the age-old problem of allocating items among different agents in a way that is efficient and fair. Two papers, by Dolev et al. and Ghodsi et al., have recently studied this problem in the context of computer systems. Both papers had similar models for agent preferences, but advocated different notions of fairness. We formalize both fairness notions in economic terms, extending them to apply to a larger family of utilities. Noting that in settings with such utilities efficiency is easily achieved in multiple ways, we study notions of fairness as criteria for choosing between different efficient allocations. Our technical results are algorithms for finding fair allocations corresponding to two fairness notions: Regarding the notion suggested by Ghodsi et al., we present a polynomial-time algorithm that computes an allocation for a general class of fairness notions, in which their notion is included. For the other, suggested by Dolev et al., we show that a competitive market equilibrium achieves the desired notion of fairness, thereby obtaining a polynomial-time algorithm that computes such a fair allocation and solving the main open problem raised by Dolev et al."]]></description>
<dc:subject>economics game-theory fairness algorithms philosophy design-patterns</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4c1ad30472a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:design-patterns"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1106.5316">
    <title>[1106.5316] Online Cake Cutting (published version)</title>
    <dc:date>2011-08-24T11:16:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1106.5316</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We propose an online form of the cake cutting problem. This models situations where agents arrive and depart during the process of dividing a resource. We show that well known fair division procedures like cut-and-choose and the Dubins-Spanier moving knife procedure can be adapted to apply to such online problems. We propose some fairness properties that online cake cutting procedures can possess like online forms of proportionality and envy-freeness. We also consider the impact of collusion between agents. Finally, we study theoretically and empirically the competitive ratio of these online cake cutting procedures. Based on its resistance to collusion, and its good performance in practice, our results favour the online version of the cut-and-choose procedure over the online version of the moving knife procedure."]]></description>
<dc:subject>game-theory economic-crisis decision-making fairness nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2a4eb42a1c0f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:economic-crisis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:decision-making"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>