Team Incentives and Bonus Floors in Relational Contracts

2020 ◽  
Vol 95 (6) ◽  
pp. 181-212
Author(s):  
Jonathan C. Glover ◽  
Hao Xue
Keyword(s):  
Performance Measures ◽  
Team Performance ◽  
Performance Measure ◽  
Relational Self ◽  
Relational Contracts ◽  
Optimal Contract ◽  
Agent Model ◽  
Strategic Complementarity ◽  
Multi Agent ◽  
The Individual

ABSTRACT Teamwork and team incentives are increasingly prevalent in modern organizations. Performance measures used to evaluate individuals' contributions to teamwork are often non-verifiable. We study a principal-multi-agent model of relational (self-enforcing) contracts in which the optimal contract resembles a bonus pool. It specifies a minimum joint bonus floor the principal is required to pay out to the agents, and gives the principal discretion to use non-verifiable performance measures to both increase the size of the pool and to allocate the pool to the agents. The joint bonus floor is useful because of its role in motivating the agents to mutually monitor each other by facilitating a strategic complementarity in their payoffs. In an extension section, we introduce a verifiable team performance measure that is a noisy version of the individual non-verifiable measures, and show that the verifiable measure is either ignored or used to create a conditional bonus floor.

Gaming Nonfinancial Performance Measures

2002 ◽  
Vol 14 (1) ◽  
pp. 119-133 ◽  
Author(s):  
Michael J. Smith
Keyword(s):  
Customer Satisfaction ◽  
Performance Measures ◽  
Performance Measure ◽  
Optimal Contract ◽  
Dual Nature ◽  
Agency Model ◽  
Current Period ◽  
Incremental Value ◽  
Nonfinancial Performance ◽  
Nonfinancial Performance Measures

This multitask agency model examines the use of nonfinancial performance measures. The first effort affects only current-period profit. The second effort affects only customer satisfaction, which increases future profits. The third effort (shifting effort) simultaneously affects both performance measures, increasing one and decreasing the other. In some cases, shifting increases the principal's expected surplus. In others, the agent uses it to “arbitrage” the contract by shifting units into the more heavily weighted performance measure. Shifting's dual nature implies that it can either increase or decrease the incremental value of customer satisfaction as a performance measure. The optimal contract may entail a negative weight on customer satisfaction.

scholarly journals Optimal Incentive Contract for Sales Team with Loss Aversion Preference

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 864
Author(s):  
Chao Li ◽  
Si-Jie Cheng ◽  
Peng-Fei Cheng
Keyword(s):  
Loss Aversion ◽  
Backward Stochastic Differential Equation ◽  
Optimal Contract ◽  
Market Demand ◽  
Agent Model ◽  
Asymmetrical Information ◽  
Manufacturing Enterprises ◽  
Sales Team ◽  
Multi Agent ◽  
Martingale Representation Theorem

When manufacturing enterprises employ sales team (or multiple salesmen) to sell products, there is asymmetric information such as the ability and efforts salesmen. Enterprises can use contracts to incentivize salesmen to work hard to maximize their profits. Assuming that market demand is sensitive to effort, and the salesman can exploit the market by increasing effort, a multi-agent model is established for the case of symmetrical information and asymmetrical information, in which the sales team has a loss aversion preference. In this multi-agent model, the agents’ utility function is non-concave and cannot be solved by traditional methods. We use a backward stochastic differential equation (BSDE) to represent agents’ contract through the martingale representation theorem and use the stochastic optimal control and matrix method to obtain the explicit solution of the optimal contract. Based on the conclusions of the research, an empirical analysis is made on the sales team of an enterprise.

scholarly journals Binary Payment Schemes: Moral Hazard and Loss Aversion

2010 ◽  
Vol 100 (5) ◽  
pp. 2451-2477 ◽  
Author(s):  
Fabian Herweg ◽  
Daniel Müller ◽  
Philipp Weinschenk
Keyword(s):  
Moral Hazard ◽  
Expected Utility ◽  
Loss Aversion ◽  
Reference Point ◽  
Performance Measure ◽  
Principal Agent ◽  
Optimal Contract ◽  
Agent Model ◽  
Payment Schemes ◽  
Principal Agent Model

We modify the principal-agent model with moral hazard by assuming that the agent is expectation-based loss averse according to Kőoszegi and Rabin (2006, 2007). The optimal contract is a binary payment scheme even for a rich performance measure, where standard preferences predict a fully contingent contract. The logic is that, due to the stochastic reference point, increasing the number of different wages reduces the agent's expected utility without providing strong additional incentives. Moreover, for diminutive occurrence probabilities for all signals the agent is rewarded with the fixed bonus if his performance exceeds a certain threshold. (JEL D82, D86, J41, M52, M12)

Deriving Performance Measures for Team Tasks: Evaluating a Methodology

1989 ◽  
Vol 33 (19) ◽  
pp. 1273-1277
Author(s):  
Joyce Hogan ◽  
Rainer M. Neubauer ◽  
Eduardo Salas
Keyword(s):  
Performance Measures ◽  
Team Performance ◽  
Performance Measure ◽  
Task Type ◽  
Performance Rating ◽  
Factor Analyses ◽  
Team Task ◽  
Different Types ◽  
Team Evaluation ◽  
Performance Conditions

This study investigates the usefulness of existing performance measures for evaluating the outcome effectiveness of team tasks. It describes a method to identify the measures most appropriate for evaluating training on different types of tasks and under different performance conditions. Six prototype team tasks served as rating stimuli that were used to evaluate 15 objective and 23 subjective team performance measures. Raters (N=33) assessed the usefulness of these performance measures for evaluating performance on each team task under three different scenarios. These scenarios asked how useful the measure would be for: (1) evaluating the performance of teams that want to improve and develop skills; (2) evaluating the performance of teams that have learned the task and need to maintain performance; and (3) helping a consultant to appraise the performance of the team. Results indicated reliable panel ratings; factor analyses of each objective and subjective performance measure correlation matrix revealed five-factor solutions for each domain, and these solutions were consistent across tasks and scenarios. Performance rating means varied significantly by task type, but generally were consistent across scenarios. The ratings are sensitive to task type and can be used systematically to specify relevant dimensions of team evaluation.

Development of a German-Language Questionnaire to Measure Collective Orientation as an Individual Attitude

2017 ◽  
Vol 76 (3) ◽  
pp. 91-105 ◽  
Author(s):  
Vera Hagemann
Keyword(s):  
Team Performance ◽  
Reliability And Validity ◽  
Future Research ◽  
German Language ◽  
Team Members ◽  
Collective Orientation ◽  
Team Research ◽  
German Speaking ◽  
Individual Variables ◽  
The Individual

Abstract. The individual attitudes of every single team member are important for team performance. Studies show that each team member’s collective orientation – that is, propensity to work in a collective manner in team settings – enhances the team’s interdependent teamwork. In the German-speaking countries, there was previously no instrument to measure collective orientation. So, I developed and validated a German-language instrument to measure collective orientation. In three studies (N = 1028), I tested the validity of the instrument in terms of its internal structure and relationships with other variables. The results confirm the reliability and validity of the instrument. The instrument also predicts team performance in terms of interdependent teamwork. I discuss differences in established individual variables in team research and the role of collective orientation in teams. In future research, the instrument can be applied to diagnose teamwork deficiencies and evaluate interventions for developing team members’ collective orientation.

Research on multi-agent model based on track methodology of ontology building

2009 ◽  
Vol 29 (2) ◽  
pp. 412-415
Author(s):  
Qiang LU ◽  
Ming CHEN ◽  
Zhi-guang WANG
Keyword(s):  
Agent Model ◽  
Model Based ◽  
Multi Agent ◽  
Ontology Building

Effects of Accounting-Method Choices on Subjective Performance-Measure Weighting Decisions: Experimental Evidence on Precision and Error Covariance

2005 ◽  
Vol 80 (4) ◽  
pp. 1163-1192 ◽  
Author(s):  
Ranjani Krishnan ◽  
Joan L. Luft ◽  
Michael D. Shields
Keyword(s):  
Performance Measures ◽  
Error Variance ◽  
Spillover Effects ◽  
Performance Measure ◽  
Incentive System ◽  
Experimental Setting ◽  
Error Covariance ◽  
Psychology Theory ◽  
Optimal Weighting ◽  
Similarity And Difference

Performance-measure weights for incentive compensation are often determined subjectively. Determining these weights is a cognitively difficult task, and archival research shows that observed performance-measure weights are only partially consistent with the predictions of agency theory. Ittner et al. (2003) have concluded that psychology theory can help to explain such inconsistencies. In an experimental setting based on Feltham and Xie (1994), we use psychology theories of reasoning to predict distinctive patterns of similarity and difference between optimal and actual subjective performance-measure weights. The following predictions are supported. First, in contrast to a number of prior studies, most individuals' decisions are significantly influenced by the performance measures' error variance (precision) and error covariance. Second, directional errors in the use of these measurement attributes are relatively frequent, resulting in a mean underreaction to an accounting change that alters performance measurement error. Third, individuals seem insufficiently aware that a change in the accounting for one measure has spillover effects on the optimal weighting of the other measure in a two-measure incentive system. In consequence, they make performance-measure weighting decisions that are likely to result in misallocations of agent effort.

EVA 2.0: Emotional and rational multimodal argumentation between virtual agents

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Niklas Rach ◽  
Klaus Weber ◽  
Yuchi Yang ◽  
Stefan Ultes ◽  
Elisabeth André ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
User Feedback ◽  
Virtual Agents ◽  
Multi Agent System ◽  
Dialogue Game ◽  
Multi Agent ◽  
The Individual ◽  
Minimal Bias ◽  
Intuitive Interface ◽  
Emotional Level

Abstract Persuasive argumentation depends on multiple aspects, which include not only the content of the individual arguments, but also the way they are presented. The presentation of arguments is crucial – in particular in the context of dialogical argumentation. However, the effects of different discussion styles on the listener are hard to isolate in human dialogues. In order to demonstrate and investigate various styles of argumentation, we propose a multi-agent system in which different aspects of persuasion can be modelled and investigated separately. Our system utilizes argument structures extracted from text-based reviews for which a minimal bias of the user can be assumed. The persuasive dialogue is modelled as a dialogue game for argumentation that was motivated by the objective to enable both natural and flexible interactions between the agents. In order to support a comparison of factual against affective persuasion approaches, we implemented two fundamentally different strategies for both agents: The logical policy utilizes deep Reinforcement Learning in a multi-agent setup to optimize the strategy with respect to the game formalism and the available argument. In contrast, the emotional policy selects the next move in compliance with an agent emotion that is adapted to user feedback to persuade on an emotional level. The resulting interaction is presented to the user via virtual avatars and can be rated through an intuitive interface.

scholarly journals A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4873
Author(s):  
Biao Xu ◽  
Minyan Lu ◽  
Hong Zhang ◽  
Cong Pan
Keyword(s):  
Wireless Sensor ◽  
Component Reliability ◽  
Agent Based ◽  
Agent Model ◽  
Cross Domain ◽  
Malware Propagation ◽  
Network Topologies ◽  
Multi Agent ◽  
Agent Characteristics ◽  
Information Domain

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

scholarly journals Speech recognition algorithm for natural language management systems under variety of accents

2020 ◽  
Vol 164 ◽  
pp. 10015
Author(s):  
Irina Gurtueva ◽  
Olga Nagoeva ◽  
Inna Pshenokova
Keyword(s):  
Speech Recognition ◽  
Recognition Algorithm ◽  
Individual Characteristics ◽  
Point Of View ◽  
Language Management ◽  
Universal System ◽  
Sound Image ◽  
Behavioral Studies ◽  
Multi Agent ◽  
The Individual

This paper proposes a concept of a new approach to the development of speech recognition systems using multi-agent neurocognitive modeling. The fundamental foundations of these developments are based on the theory of cognitive psychology and neuroscience, and advances in computer science. The purpose of this work is the development of general theoretical principles of sound image recognition by an intelligent robot and, as the sequence, the development of a universal system of automatic speech recognition, resistant to speech variability, not only with respect to the individual characteristics of the speaker, but also with respect to the diversity of accents. Based on the analysis of experimental data obtained from behavioral studies, as well as theoretical model ideas about the mechanisms of speech recognition from the point of view of psycholinguistic knowledge, an algorithm resistant to variety of accents for machine learning with imitation of the formation of a person’s phonemic hearing has been developed.

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