Profit-aware Team Grouping in Social Networks: A Generalized Cover Decomposition Approach

In this paper, we investigate the profit-aware team grouping problem in social networks. We consider a setting in which people possess different skills and compatibility among these individuals is captured by a social network. Here, we assume a collection of tasks, where each task requires a specific set of skills, and yields a different profit upon completion. Active and qualified individuals may collaborate with each other in the form of \emph{teams} to accomplish a set of tasks. Our goal is to find a grouping method that maximizes the total profit of the tasks that these teams can complete. Any feasible grouping must satisfy the following three conditions: (i) each team possesses all skills required by the task, (ii) individuals within the same team are social compatible, and (iii) each individual is not overloaded. We refer to this as the \textsc{TeamGrouping} problem. Our work presents a detailed analysis of the computational complexity of the problem, and propose a LP-based approximation algorithm to tackle it and its variants. Although we focus on team grouping in this paper, our results apply to a broad range of optimization problems that can be formulated as a cover decomposition problem

"When Knowledge is an Asset: Explaining the Organizational Structure of Large Law Firms"

We study the economics of employment relationships through theoretical and empirical analyses of an unusual set of firms, large law firms. Our point of departure is the "property rights" approach that emphasizes the centrality of ownership's legal rights to control important, nonhuman assets of the enterprise. From this perspective, large law firms are an interesting and potentially important object of study, because the most valuable assets of these firms take the form of knowledge--particularly knowledge of the needs and interests of clients. We argue that the two most distinctive organizational features of large law firms, the use of "up or out" promotion contests and the practice of having winners become residual claimants in the firm, emerge naturally in this setting. In addition to explaining otherwise anomalous features of the up-or-out partnership system, this paper suggests a general framework for analyzing organizations where assets reside in the brains of employees.

When Knowledge is an Asset: Explaining the Organizational Structure of Large Law Firms

We study the economics of employment relationships through theoretical and empirical analysis of an unusual set of firms, large law firms. Our point of departure is the "property rights" approach that emphasizes the centrality of ownership's legal rights to control important, non-human assets of the enterprise. From this perspective, large law firms are an interesting and potentially important object of study because the most valuable assets of these firms take the form of knowledge - particularly knowledge of the needs and interests of clients. We argue that the two most distinctive organizational features of large law firms, the use of "up or out" promotion contests and the practice of having winners become residual claimants in the firm, emerge naturally in this setting. In addition to explaining otherwise anomalous features of the up-or-out partnership system, this paper suggests a general framework for analyzing organizations where assets reside in the brains of employees.

Productive Cluster Hire

Discovering a group of experts to complete a set of tasks that require various skills is known as Cluster Hire Problem. Each expert has a set of skills which he/she can offer and charges a monetary cost to offer their expertise. We are given a set of projects that need to be completed and on completion of each project, the organization gets a Profit. For performing a subset of given projects, we are given a predetermined budget. This budget is spent on hiring experts. We extend this problem by introducing the productivity and capacity of experts. We want to hire experts that are more productive, and this factor is determined on the basis of their past experience. We also want to make sure that no expert is overworked as it is not possible for a single expert to provide his/her expertise for unlimited times. Our goal is to hire as many experts as possible in which the sum of their hiring costs (i.e., salary) is under the given budget as we are interested to maximize the profit and also maximize the productivity of the group of experts, our problem is a bi-objective optimization problem. To achieve this, we propose two different approaches that maximize our Profit and Productivity

Building Teams of Experts using Integer Linear Programming

Given a set of projects, each requiring a set of specific skills, and given a set of experts, each possessing a set of specific skills, the cluster hire in a network of experts seeks to find a suitable subset of the experts to jointly accomplish a subset of the given projects with their complementary expertise. We consider the problem of selecting an optimal team of the experts in terms of maximizing the profit that the selected team is able to generate, where the profit is determined partly by the revenue of the projects this team is able to accomplish, partly by the efficiency of the team measured by the prior collaboration experience among its team members. This optimization is further constrained by the given workload capacity of each expert, and by a given budget on team hiring. We approach the optimal solution with Integer Linear Programming (ILP) technique and compare its result with those from other heuristic solutions

Screening, Competition, and Job Design

In recent decades, many firms offered more discretion to their employees, often increasing the productivity of effort but also leaving more opportunities for shirking. These “high-performance work systems” are difficult to understand in terms of standard moral hazard models. We show experimentally that complementarities between high effort discretion, rent-sharing, screening opportunities, and competition are important driving forces behind these new forms of work organization. We document in particular the endogenous emergence of two fundamentally distinct types of employment strategies. Employers either implement a control strategy, which consists of low effort discretion and little or no rent-sharing, or they implement a trust strategy, which stipulates high effort discretion and substantial rent-sharing. If employers cannot screen employees, the control strategy prevails, while the possibility of screening renders the trust strategy profitable. The introduction of competition substantially fosters the trust strategy, reduces market segmentation, and leads to large welfare gains for both employers and employees

Collective Choice and Control Rights in Firms

Recent writers have asserted that firms controlled by workers are rare because workers have diverse preferences over firm policies, and thus suffer from high transaction costs in making collective decisions. This is contrasted with firms controlled by investors, who all support the goal of wealth maximization. However, the source of the asymmetry between capital and labor has not been clearly identified. For example, firms could attract labor inputs by selling transferable shares, and well-known unanimity theorems from the finance literature carry over to models of this kind. We resolve this puzzle by arguing that because financial capital is exceptionally mobile, capital markets are sufficiently competitive to induce unanimity. The lower mobility of human capital implies that labor markets are monopolistically competitive and hence that unanimity cannot be expected in labor-managed firms. Moreover, such firms are vulnerable to takeover by investors while capital-managed firms are substantially less vulnerable to takeover by workers.capitalist firms, labor-managed firms, collective choice, preference heterogeneity, unanimity, voting, membership markets, control rights

Firm Size, Productivity, and Manager Wages: A Job Assignment Approach

Ability of managers and other nonproduction professionals is key for the productivity of firms. Hence, the assignment of heterogeneous nonproduction workers across firms determines the distribution of productivity. In turn, the transmission of productivity differences into profit differences -- resulting from product market competition -- determines firms' willingness to pay for higher managerial skills. This paper explores the equilibrium assignment of nonproduction workers across ex ante identical firms which results from this interaction between product market and the market for nonproduction skills. The analysis suggests that, typically, large and productive firms coexist with small, low-productivity firms. Consistent with empirical evidence, a skewed distribution of firm size tends to arise. Moreover, the model predicts a positive relationship of firm size to productivity, manager quality, and manager remuneration. Finally, according to comparative-static analysis, higher intensity of product market competition can account for increases in the compensation at the top of the wage distributio

Team Formation for Scheduling Educational Material in Massive Online Classes

Whether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective, 1 group students so that they can maximally benefit from peer interaction and 2 find an optimal schedule of the educational material for each group. Thus, in this paper, we solve the problem of team formation and content scheduling for education. Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students. The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group. We show this problem to be NP-hard and develop a polynomial algorithm for it. We show our algorithm to be effective both on synthetic as well as a real data set. For our experiments, we use real data on students' grades in a Computer Science department. As part of our contribution, we release a semi-synthetic dataset that mimics the properties of the real data