Social Choice for Partial Preferences Using Imputation

Within the field of multiagent systems, the area of computational social choice considers the problems arising when decisions must be made collectively by a group of agents. Usually such systems collect a ranking of the alternatives from each member of the group in turn, and aggregate these individual rankings to arrive at a collective decision. However, when there are many alternatives to consider, individual agents may be unwilling, or unable, to rank all of them, leading to decisions that must be made on the basis of incomplete information. While earlier approaches attempt to work with the provided rankings by making assumptions about the nature of the missing information, this can lead to undesirable outcomes when the assumptions do not hold, and is ill-suited to certain problem domains. In this thesis, we propose a new approach that uses machine learning algorithms (both conventional and purpose-built) to generate plausible completions of each agent’s rankings on the basis of the partial rankings the agent provided (imputations), in a way that reflects the agents’ true preferences. We show that the combination of existing social choice functions with certain classes of imputation algorithms, which forms the core of our proposed solution, is equivalent to a form of social choice. Our system then undergoes an extensive empirical validation under 40 different test conditions, involving more than 50,000 group decision problems generated from real-world electoral data, and is found to outperform existing competitors significantly, leading to better group decisions overall. Detailed empirical findings are also used to characterize the behaviour of the system, and illustrate the circumstances in which it is most advantageous. A general testbed for comparing solutions using real-world and artificial data (Prefmine) is then described, in conjunction with results that justify its design decisions. We move on to propose a new machine learning algorithm intended specifically to learn and impute the preferences of agents, and validate its effectiveness. This Markov-Tree approach is demonstrated to be superior to imputation using conventional machine learning, and has a simple interpretation that characterizes the problems on which it will perform well. Later chapters contain an axiomatic validation of both of our new approaches, as well as techniques for mitigating their manipulability. The thesis concludes with a discussion of the applicability of its contributions, both for multiagent systems and for settings involving human elections. In all, we reveal an interesting connection between machine learning and computational social choice, and introduce a testbed which facilitates future research efforts on computational social choice for partial preferences, by allowing empirical comparisons between competing approaches to be conducted easily, accurately, and quickly. Perhaps most importantly, we offer an important and effective new direction for enabling group decision making when preferences are not completely specified, using imputation methods

The meaning of gainful trade

'Gainful trade: a new economics' explains why and how economics may be rebuilt from scratch to allow the occurence of gainful trade. It uses a new concept of consistency of social choice and a new analytical tool called consistency analysis to build a unified model of economics. It answers all questions of macroeconomics from an extended micro model. The new paradigm makes macroeconomics obsolete. The key element is the treatment of money as a means of payment to allow indirect exchange.Trade, gains, consistent choice, money as a means of payment, entrepreneurship, allocation,

Sources of Advantageous Selection: Evidence from the Medigap Insurance Market

We provide strong evidence of advantageous selection in the Medigap insurance market, and analyze its sources. Using Medicare Current Beneficiary Survey (MCBS) data, we find that, conditional on controls for the price of Medigap, medical expenditures for senior citizens with Medigap coverage are, on average, about $4,000 less than for those without. But, if we condition on health, expenditures for seniors on Medigap are about$2,000 more. These two findings can only be reconciled if those with less health expenditure risk are more likely to purchase Medigap, implying advantageous selection. By combining the MCBS and the Health and Retirement Study (HRS), we investigate the sources of this advantageous selection. These include income, education, longevity expectations and financial planing horizons, as well as cognitive ability. Once we condition on all these factors, seniors with higher expected medical expenditure are indeed more likely to purchase Medigap. Surprisingly, risk preferences do not appear to be a source of advantageous selection. But cognitive ability emerges as a particularly important factor, consistent with a view that many senior citizens have difficulty understanding Medicare and Medigap rules.

Consumption inequality and partial insurance

This paper uses panel data on household consumption and income to evaluate the degree of insurance to income shocks. Our aim is to describe the transmission of income inequality into consumption inequality by contrasting shifts in the cross-sectional distribution of income growth with shifts in the cross-sectional distribution of consumption growth. We combine panel data on income from the PSID with consumption data from repeated CEX cross-sections. The results point to some partial insurance but reject the complete market restrictions. We find a greater degree of insurance for transitory shocks and differences in the degree of insurance over time and across demographic groups. We also document the importance of durables and of taxes and transfers as a means of insurance

Consumption inequality and partial insurance

This paper examines the transmission of income inequality into consumption inequality and in so doing investigates the degree of insurance to income shocks. Panel data on income from the PSID is combined with consumption data from repeated CEX cross-sections to identify the degree of insurance to permanent and transitory shocks. In the process we also present new evidence of the growth in the variance of permanent and transitory shocks in the US during the 1980s. We find some partial insurance of permanent income shocks with more insurance possibilities for the college educated and those nearing retirement. We find little evidence against full insurance for transitory income shocks except among low income households. Tax and welfare benefits are found to play an important role in insuring permanent shocks. Adding durable expenditures to the consumption measure suggests that durable replacement is an important insurance mechanism, especially for transitory income shocks

Comparing Election Methods Where Each Voter Ranks Only Few Candidates

Election rules are formal processes that aggregate voters preferences, typically to select a single candidate, called the winner. Most of the election rules studied in the literature require the voters to rank the candidates from the most to the least preferred one. This method of eliciting preferences is impractical when the number of candidates to be ranked is large. We ask how well certain election rules (focusing on positional scoring rules and the Minimax rule) can be approximated from partial preferences collected through one of the following procedures: (i) randomized-we ask each voter to rank a random subset of $\ell$ candidates, and (ii) deterministic-we ask each voter to provide a ranking of her $\ell$ most preferred candidates (the $\ell$-truncated ballot). We establish theoretical bounds on the approximation ratios and we complement our theoretical analysis with computer simulations. We find that mostly (apart from the cases when the preferences have no or very little structure) it is better to use the randomized approach. While we obtain fairly good approximation guarantees for the Borda rule already for $\ell = 2$, for approximating the Minimax rule one needs to ask each voter to compare a larger set of candidates in order to obtain good guarantees

Decision Aid Implementation and Patients' Preferences for Hip and Knee Osteoarthritis Treatment: Insights from the High Value Healthcare Collaborative.

Background:Shared decision making (SDM) research has emphasized the role of decision aids (DAs) for helping patients make treatment decisions reflective of their preferences, yet there have been few collaborative multi-institutional efforts to integrate DAs in orthopedic consultations and primary care encounters. Objective:In the context of routine DA implementation for SDM, we investigate which patient-level characteristics are associated with patient preferences for surgery versus medical management before and after exposure to DAs. We explored whether DA implementation in primary care encounters was associated with greater shifts in patients' treatment preferences after exposure to DAs compared to DA implementation in orthopedic consultations. Design:Retrospective cohort study. Setting:10 High Value Healthcare Collaborative (HVHC) health systems. Study participants:A total of 495 hip and 1343 adult knee osteoarthritis patients who were exposed to DAs within HVHC systems between July 2012 to June 2015. Results:Nearly 20% of knee patients and 17% of hip patients remained uncertain about their treatment preferences after viewing DAs. Older patients and patients with high pain levels had an increased preference for surgery. Older patients receiving DAs from three HVHC systems that transitioned DA implementation from orthopedics into primary care had lower odds of preferring surgery after DA exposure compared to older patients in seven HVHC systems that only implemented DAs for orthopedic consultations. Conclusion:Patients' treatment preferences were largely stable over time, highlighting that DAs for SDM largely do not necessarily shift preferences. DAs and SDM processes should be targeted at older adults and patients reporting high pain levels. Initiating treatment conversations in primary versus specialty care settings may also have important implications for engagement of patients in SDM via DAs