68,114 research outputs found
Optimizing Expected Utility in a Multinomial Logit Model with Position Bias and Social Influence
Motivated by applications in retail, online advertising, and cultural
markets, this paper studies how to find the optimal assortment and positioning
of products subject to a capacity constraint. We prove that the optimal
assortment and positioning can be found in polynomial time for a multinomial
logit model capturing utilities, position bias, and social influence. Moreover,
in a dynamic market, we show that the policy that applies the optimal
assortment and positioning and leverages social influence outperforms in
expectation any policy not using social influence
Measuring time preferences
We review research that measures time preferences—i.e., preferences over intertemporal tradeoffs. We distinguish between studies using financial flows, which we call “money earlier or later” (MEL) decisions and studies that use time-dated consumption/effort. Under different structural models, we show how to translate what MEL experiments directly measure (required rates of return for financial flows) into a discount function over utils. We summarize empirical regularities found in MEL studies and the predictive power of those studies. We explain why MEL choices are driven in part by some factors that are distinct from underlying time preferences.National Institutes of Health (NIA R01AG021650 and P01AG005842) and the Pershing Square Fund for Research in the Foundations of Human Behavior
How do top- and bottom-performing companies differ in using business analytics?
Purpose
Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA.
Design/methodology/approach
Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies.
Findings
Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.
Practical implications
Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities.
Originality/value
This study provides useful management insights into the effective use of BA for improving organizational performance
Equity of Attention: Amortizing Individual Fairness in Rankings
Rankings of people and items are at the heart of selection-making,
match-making, and recommender systems, ranging from employment sites to sharing
economy platforms. As ranking positions influence the amount of attention the
ranked subjects receive, biases in rankings can lead to unfair distribution of
opportunities and resources, such as jobs or income.
This paper proposes new measures and mechanisms to quantify and mitigate
unfairness from a bias inherent to all rankings, namely, the position bias,
which leads to disproportionately less attention being paid to low-ranked
subjects. Our approach differs from recent fair ranking approaches in two
important ways. First, existing works measure unfairness at the level of
subject groups while our measures capture unfairness at the level of individual
subjects, and as such subsume group unfairness. Second, as no single ranking
can achieve individual attention fairness, we propose a novel mechanism that
achieves amortized fairness, where attention accumulated across a series of
rankings is proportional to accumulated relevance.
We formulate the challenge of achieving amortized individual fairness subject
to constraints on ranking quality as an online optimization problem and show
that it can be solved as an integer linear program. Our experimental evaluation
reveals that unfair attention distribution in rankings can be substantial, and
demonstrates that our method can improve individual fairness while retaining
high ranking quality.Comment: Accepted to SIGIR 201
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