55,902 research outputs found

    Incomplete Incentives, Task Temporality, and Effort Spillover in a Multitask Environment

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    Incomplete incentive contracts in multitask environments present a significant control challenge of ensuring that employees expend sufficient effort towards all assigned tasks, particularly those that are not directly incentivized. Prior research finds that the severity of this agency issue depends on task temporality such that it is less problematic when the tasks are performed concurrently as opposed to sequentially. I extend the literature by examining how incentive type, task temporality, and performance feedback influence effort spillover onto a second, unincentivized task. Specifically, I predict that goal-based incentives and positive performance feedback on an incentivized task will lead to a stronger positive affective response, which will induce greater effort spillover onto an unincentivized task, under sequential multitasking relative to concurrent multitasking. To test my predictions, I employ a 2 x 2 between-subjects experimental design, where I manipulate the type of incentive contract used for the incentivized task between goal-based or piece-rate incentives and task temporality between concurrent or sequential. Participants complete two real-effort tasks where Task 1 performance is incentivized, and Task 2 performance is unincentivized. I examine the impact of my manipulations on participants’ affective responses to performance feedback on the incentivized task and their performance on the unincentivized task, which proxies for task effort, as my dependent variables of interest. I find that goal-based incentives under sequential multitasking following goal attainment does lead to greater effort spillover onto an unincentivized task under sequential multitasking compared to concurrent multitasking. Consistent with my theory, I find that positive affect from performance feedback is positively associated with effort spillover onto an unincentivized task. I further predict that goal-based incentives and negative performance feedback on an incentivized task is associated with a stronger negative affective response, which will induce lower effort spillover onto an unincentivized task under sequential multitasking relative to concurrent multitasking. However, I do not find support for the prediction. Specifically, I do not find evidence that negative affect following negative performance feedback is associated with negative effort spillover onto an unincentivized task. The findings from this study highlight the importance of examining how features of the management control system (i.e., incentive type, performance feedback, and job design) can help to address a costly agency problem in multitask environments

    Strategies for prediction under imperfect monitoring

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    We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but rather to a feedback signal. The proposed strategies are consistent in the sense that they achieve, asymptotically, the best possible average reward. It was Rustichini (1999) who first proved the existence of such consistent predictors. The forecasters presented here offer the first constructive proof of consistency. Moreover, the proposed algorithms are computationally efficient. We also establish upper bounds for the rates of convergence. In the case of deterministic feedback, these rates are optimal up to logarithmic terms.Comment: Journal version of a COLT conference pape

    LRMM: Learning to Recommend with Missing Modalities

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    Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201

    Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

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    This paper develops methods for estimating dynamic structural microeconomic models with serially correlated latent state variables. The proposed estimators are based on sequential Monte Carlo methods, or particle filters, and simultaneously estimate both the structural parameters and the trajectory of the unobserved state variables for each observational unit in the dataset. We focus two important special cases: single agent dynamic discrete choice models and dynamic games of incomplete information. The methods are applicable to both discrete and continuous state space models. We first develop a broad nonlinear state space framework which includes as special cases many dynamic structural models commonly used in applied microeconomics. Next, we discuss the nonlinear filtering problem that arises due to the presence of a latent state variable and show how it can be solved using sequential Monte Carlo methods. We then turn to estimation of the structural parameters and consider two approaches: an extension of the standard full-solution maximum likelihood procedure (Rust, 1987) and an extension of the two-step estimation method of Bajari, Benkard, and Levin (2007), in which the structural parameters are estimated using revealed preference conditions. Finally, we introduce an extension of the classic bus engine replacement model of Rust (1987) and use it both to carry out a series of Monte Carlo experiments and to provide empirical results using the original data.dynamic discrete choice, latent state variables, serial correlation, sequential Monte Carlo methods, particle filtering

    sk_p: a neural program corrector for MOOCs

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    We present a novel technique for automatic program correction in MOOCs, capable of fixing both syntactic and semantic errors without manual, problem specific correction strategies. Given an incorrect student program, it generates candidate programs from a distribution of likely corrections, and checks each candidate for correctness against a test suite. The key observation is that in MOOCs many programs share similar code fragments, and the seq2seq neural network model, used in the natural-language processing task of machine translation, can be modified and trained to recover these fragments. Experiment shows our scheme can correct 29% of all incorrect submissions and out-performs state of the art approach which requires manual, problem specific correction strategies

    Tuning Windowed Chi-Squared Detectors for Sensor Attacks

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    A model-based windowed chi-squared procedure is proposed for identifying falsified sensor measurements. We employ the widely-used static chi-squared and the dynamic cumulative sum (CUSUM) fault/attack detection procedures as benchmarks to compare the performance of the windowed chi-squared detector. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors do not raise alarms (zero-alarm attacks). We quantify the advantage of using dynamic detectors (windowed chi-squared and CUSUM detectors), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations using a chemical reactor are presented to illustrate the performance of our tools
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