An experiment on markets and contracts : do social preferences determine corporate culture?

This paper reports experimental evidence on a stylized labor market. The experiment is designed as a sequence of three treatments. In the last treatment, TR3, four principals, who face four teams of two agents, compete by offering the agents a contract from a fixed menu. In this menu, each contract is the optimal solution of a (complete information) mechanism design problem where principals face agents’ who have social (i.e. interdependent) distributional preferences a’ la Fehr and Schmidt [19] with a specific parametrization. Each agent selects one of the available contracts offered by the principals (i.e. he “chooses to work” for a principal). Production is determined by the outcome of a simple effort game induced by the chosen contract. In the first two treatments, TR1 and TR2, we estimate individual social preference parameters and beliefs in the effort game, respectively. We find that social preferences are significant determinants of the matching process between labor supply and demand in the market stage, as well as principals’ and agents’ contract and effort decisions. In addition, we also see that social preferences explain the matching process in the labor market, as agents display a higher propensity to choose to work for a principal with similar distributional preferences.

Social Preferences and Strategic Uncertainty: An Experiment on Markets and Contracts

This paper reports experimental evidence on a stylized labor market. The experiment is designed as a sequence of three phases. In the rst two phases, P1 and P2; agents face simple games, which we use to estimate subjects social and reciprocity concerns, together with their beliefs. In the last phase, P3; four principals, who face four teams of two agents, compete by o¤ering agents a contract from a xed menu. Then, each agent selects one of the available contracts (i.e. he "chooses to work" for a principal). Production is determined by the outcome of a simple effort game induced by the chosen contract. We nd that (heterogeneous) social preferences are signi cant determinants of choices in all phases of the experiment. Since the available contracts display a trade-of between fairness and strategic uncertainty, we observe that the latter is a much stronger determinant of choices, for both principals and agents. Finally, we also see that social preferences explain, to a large extent, matching between principals and agents, since agents display a marked propensity to work for principals with similar social preferences

A conceptual framework and protocol for defining clinical decision support objectives applicable to medical specialties.

BackgroundThe U.S. Centers for Medicare and Medicaid Services established the Electronic Health Record (EHR) Incentive Program in 2009 to stimulate the adoption of EHRs. One component of the program requires eligible providers to implement clinical decision support (CDS) interventions that can improve performance on one or more quality measures pre-selected for each specialty. Because the unique decision-making challenges and existing HIT capabilities vary widely across specialties, the development of meaningful objectives for CDS within such programs must be supported by deliberative analysis.DesignWe developed a conceptual framework and protocol that combines evidence review with expert opinion to elicit clinically meaningful objectives for CDS directly from specialists. The framework links objectives for CDS to specialty-specific performance gaps while ensuring that a workable set of CDS opportunities are available to providers to address each performance gap. Performance gaps may include those with well-established quality measures but also priorities identified by specialists based on their clinical experience. Moreover, objectives are not constrained to performance gaps with existing CDS technologies, but rather may include those for which CDS tools might reasonably be expected to be developed in the near term, for example, by the beginning of Stage 3 of the EHR Incentive program. The protocol uses a modified Delphi expert panel process to elicit and prioritize CDS meaningful use objectives. Experts first rate the importance of performance gaps, beginning with a candidate list generated through an environmental scan and supplemented through nominations by panelists. For the highest priority performance gaps, panelists then rate the extent to which existing or future CDS interventions, characterized jointly as "CDS opportunities," might impact each performance gap and the extent to which each CDS opportunity is compatible with specialists' clinical workflows. The protocol was tested by expert panels representing four clinical specialties: oncology, orthopedic surgery, interventional cardiology, and pediatrics

Social Preferences and Strategic Uncertainty: An Experiment on Markets and Contracts

This paper reports experimental evidence on a stylized labor market. The experiment is designed as a sequence of three phases. In the rst two phases, P1 and P2; agents face simple games, which we use to estimate subjects social and reciprocity concerns, together with their beliefs. In the last phase, P3; four principals, who face four teams of two agents, compete by o¤ering agents a contract from a xed menu. Then, each agent selects one of the available contracts (i.e. he "chooses to work" for a principal). Production is determined by the outcome of a simple effort game induced by the chosen contract. We nd that (heterogeneous) social preferences are signi cant determinants of choices in all phases of the experiment. Since the available contracts display a trade-of between fairness and strategic uncertainty, we observe that the latter is a much stronger determinant of choices, for both principals and agents. Finally, we also see that social preferences explain, to a large extent, matching between principals and agents, since agents display a marked propensity to work for principals with similar social preferences

Social Preferences and Strategic Uncertainty: An Experiment on Markets and Contracts

This paper reports experimental evidence on a stylized labor market. The experiment is designed as a sequence of three phases. In the first two phases, P1 and P2; agents face simple games, which we use to estimate subjects' social and reciprocity concerns, together with their beliefs. In the last phase, P3; four principals, who face four teams of two agents, compete by offering agents a contract from a fixed menu. Then, each agent selects one of the available contracts (i.e. he "chooses to work" for a principal). Production is determined by the outcome of a simple effort game induced by the chosen contract. We find that (heterogeneous) social preferences are significant determinants of choices in all phases of the experiment. Since the available contracts display a trade-off between fairness and strategic uncertainty, we observe that the latter is a much stronger determinant of choices, for both principals and agents. Finally, we also see that social preferences explain, to a large extent, matching between principals and agents, since agents display a marked propensity to work for principals with similar social preferences.social preferences; team incentives; mechanism design; experimental economics

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and use of a performance-based approach in transportation planning and programming. This research introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to (1) improve bridge management, (2) support state departments of transportation with their transition efforts to comply with the MAP-21 requirements, (3) determine short- and long-term intervention strategies and funding requirements, and (4) facilitate trade-offs between funding levels and performance. The proposed methodology focuses on one transportation asset class (i.e., bridge) and is structured around the following five modules: 1. Data Processing Module, 2. Improvement Module, 3. Element-level Optimization Module, 4. Bridge-level Optimization Module, and 5. Network-level Optimization Module. To overcome computer memory and processing time limitations, the methodology relies on the following three distinct screening processes: 1. Element Deficiency Process, 2. Alternative Feasibility Process, and 3. Solution Superiority Screening Process. The methodology deploys an independent deterioration model (i.e., Weibull/Markov model), to predict performance, and a life-cycle cost model, to estimate life-cycle costs and benefits. Life-cycle (LC) alternatives (series of element improvement actions) are generated based on a new simulation arrangement for three distinct improvement types: 1. maintenance, repair and rehabilitation (preservation); 2. functional improvement; and 3. replacement. A LC activity profile is constructed separately for each LC alternative action path. The methodology consists of three levels of optimization assessment based on the Pareto optimality concept: (1) an element-level optimization, to identify optimal or near-optimal element intervention actions for each deficient element (poor condition state) of a candidate bridge; (2) a bridge-level optimization, to identify combinations of optimal or near-optimal element intervention actions for a candidate bridge; and (3) a network-level optimization, following either a top-down or bottom-up approach, to identify sets of optimal or near-optimal element intervention actions for a network of bridges. A robust metaheuristic genetic algorithm (i.e., Non-dominated Sorting Genetic Algorithm II, [NSGA-II]) is deployed to handle the large size of multi-objective optimization problems. A MATLAB-based tool prototype was developed to test concepts, demonstrate effectiveness, and communicate benefits. Several examples of unconstrained and constrained scenarios were established for implementing the methodology using the tool prototype. Results reveal the capability of the proposed EB-MOO methodology to generate a high quality of Pareto optimal or near-optimal solutions, predict performance, and determine appropriate intervention actions and funding requirements. The five modules collectively provide a systematic process for the development and evaluation of improvement programs and transportation plans. Trade-offs between Pareto optimal or near-optimal solutions facilitate identifying best investment strategies that address short- and long-term goals and objective priorities

Capacity allocation and downsizing decisions in project portfolio management.

This paper aims to gain insight into capacity allocation and downsizing decisions in project portfolio management. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. We first determine the amount of critical capacity that is optimally allocated to strategic projects with deterministic or stochastic workloads for a single-period problem when the impact of downsizing is known. In order to solve the multi-period problem, we have modeled the behavior of the portfolio in subsequent periods as a single project for which the return on investment can be estimated. Secondly, we investigate how the scarcity of resources affects the (expected) value of projects. The independent (expected) project value is calculated under the assumption of unlimited capacity; in contrast, the dependent (expected) project value incorporates the resource constraints. We find that the dependent project value is equal to the independent project value when the return on investment of the portfolio is sufficiently low. In addition, we determine the relation between the return on investment of the portfolio and the value of a project and conclude that the impact of resource scarcity on the value of a project cannot be fully captured by the common financial practice of adapting the discount rate with the estimated return on investment.Project portfolio management; Downsizing; Stochastic workload;