Self-interested Decision Makers Research Articles

Community-aware empathetic social choice for social network group decision making

To study how interdependent and self-interested decision makers collectively make a choice in highly complex social environments, this paper presents a novel and powerful Community-Aware Empathetic Social Choice (CAESC) model, which considers decision makers in social networks as autonomy-oriented agents, who derive utility based on both their own intrinsic preferences, and empathetic preferences determined by the satisfaction of their intra-community neighbors. We devise optimization approaches for identifying community structures with local (global) social welfare maximizing under two varieties of CAESC models: The local CAESC model first identifies the non-cooperative behaviors by calculating the structure-preference coordination coefficients between pairwise decision makers, then employs the Logit response like dynamics process in a carefully defined potential game to find the optimal community structure that maximizes the local social welfare. To find the optimal community structure that maximizes the global social welfare, the global CAESC model is composed of three coupled phases during each stage: (i) opinion evolution within each community using the classic Jacobi method; (ii) structure-preference coordination matrix updating based on the current opinion vector; and (iii) local optimal community structure updating based on the local CAESC model. Extensive experiments on both randomly generated and real-world social networks with synthetic and real-world preferences confirm that neglecting the empathetic effect and community structure usually yields sub-optimal group decisions which degrade the social welfare of network members. Our experiments also show that, the higher societal empathy, the rougher preference distribution and the denser network structure will facilitate the maximal social welfare reaching in both local and global CAESC models; meanwhile, by comparing with the typical empathetic decision making models and some recent proposed consensus reaching methods, our proposed approaches show relatively significant advantages in terms of candidate ranking and decision efficiency.

Computing equilibria for integer programming games

The recently-defined class of integer programming games (IPG) models situations where multiple self-interested decision makers interact, with their strategy sets represented by a finite set of linear constraints together with integer requirements. Many real-world problems can suitably be cast in this way, hence anticipating IPG outcomes is of crucial value for policy makers. Nash equilibria have been widely accepted as the solution concept of a game. Thus, their computation provides a reasonable prediction of games outcome.In this paper, we start by showing the computational complexity of deciding the existence of a Nash equilibrium for an IPG. Then, using sufficient conditions for their existence, we develop a general algorithmic approach that is guaranteed to return a Nash equilibrium when the game is finite and to approximate an equilibrium when payoff functions are Lipschitz continuous. We also showcase how our methodology can be changed to determine other types of equilibria. The performance of our methods is analyzed through computational experiments on knapsack, kidney exchange and a competitive lot-sizing games. To the best of our knowledge, this is the first time that equilibria computation methods for general IPGs have been designed and computationally tested.

Knowledge Asset Outsourcing Decision

When making decisions on whether to outsource managers are expected to consider theoretical variables like asset specificity from transaction cost economics (TCE) and core-competence from resource based theory (RBT). While these expectation have been found consistently valid for physical assets, they have been inconsistent for knowledge assets. We use a vignette based experiment to test if the lack of consistency is due to the limitation in the theoretical prescriptions or a result of peculiarities of knowledge assets. We use rigorous implementation of weighted least square estimator with mean and variance (WLSMV) based structural equation modeling to arrive at the results. We show that, for knowledge assets, while the TCE and RBT are valid, the operationalization of the idea of asset specificity and core-competence is heterogeneous. For RBT, the heterogeneity is caused by the involvement of decision maker with the knowledge asset. We resolve the lack of clarity of TCE and RBT in knowledge asset outsourcing decisions by showing that, possibly because of intangibility and peculiarities of knowledge assets, the operationalization of theoretical variables is systematically biased. Additionally, we show that decision makers can intentionally subvert knowledge asset outsourcing decisions towards self-interest. This finding questions the implicit assumption of TCE and RBT that decision makers are altruistic stewards and not self-interested agents. Our findings suggest a detailed analysis of individual decision making for knowledge asset outsourcing and more interestingly create an intrigue about how the biased and self-interested decision makers would interact to arrive at a firm level knowledge outsourcing decision.

How Can New Governance Regulation Develop? Regulatory Dialectics and Mandatory Charity Performance Reporting

Increasingly New Governance approaches are evident in public administration, in contrast to the New Public Management (NPM) approach and reforms of last century. In focusing on competition and outcomes, regulation under NPM has been a tool to manage self-interested decision-makers, and is beset by conflict. Kane’s model of regulatory dialectics could be applied to this approach. New Governance (variously known as New Public Governance, Public Value Governance) takes a process approach, aimed at problem solving and co-creating public good. It blurs traditional regulatory boundaries, and yet, the concern is that, when developing mandatory regulation, power imbalances may still occur and that the process approach may severely delay successful outcomes. We propose a New Governance-orientated model of regulatory dialectics. Here, the use of formal organisations, routine processes and informal dialogues facilitates repeated interactions, identifying more ‘soft’ than ‘hard’ responses by regulators and regulates. This less adversarial and more partnered process leads to greater engagement in regulatory development which impacts significantly on the regulation that results, and has the potential to improve acceptance of (and compliance with) mandatory regulation.

Decentralized Q-Learning for Stochastic Teams and Games

There are only a few learning algorithms applicable to stochastic dynamic teams and games which generalize Markov decision processes to decentralized stochastic control problems involving possibly self-interested decision makers. Learning in games is generally difficult because of the non-stationary environment in which each decision maker aims to learn its optimal decisions with minimal information in the presence of the other decision makers who are also learning. In stochastic dynamic games, learning is more challenging because, while learning, the decision makers alter the state of the system and hence the future cost. In this paper, we present decentralized Q-learning algorithms for stochastic games, and study their convergence for the weakly acyclic case which includes team problems as an important special case. The algorithms are decentralized in that each decision maker has access only to its own decisions and cost realizations as well as the state transitions; in particular, each decision maker is completely oblivious to the presence of the other decision makers. We show that these algorithms converge to equilibrium policies almost surely in large classes of stochastic games.

Joint optimization for coordinated configuration of product families and supply chains by a leader-follower Stackelberg game

Product family design by module configuration is conducive to accommodating product variety while maintaining mass production efficiency. Effective fulfillment of product families necessitates joint decision making of product family configuration (PFC) and downstream supply chain configuration (SCC), due to nowadays manufacturers’ moving towards assembly-to-order production throughout a distributed supply chain network. Existing decision models for joint optimization of product family and supply chain configuration are originated from an “all-in-one” approach that assumes both PFC and SCC decisions can be integrated into one optimization problem by aggregating two different types of objectives into a single objective function. Such an assumption neglects the complex tradeoffs underlying two different decision making problems and fails to reveal the inherent coupling of PFC and SCC.This paper formulates joint configuration of a product family and its supply chain as a leader-follower Stackelberg game that is enacted through a bi-level hierarchical optimization mechanism to model the coordination between two self-interested decision makers for PFC and SCC. The PFC decisions are modeled as an upper-level optimization problem (called leader) for optimal selection of modules, module instances, and product variants. The SCC decisions are modeled as a lower-level optimization problem (called follower), which responds to decisions of the upper level in order to determine an optimal supply chain configuration and inventory policies. A nonlinear, mixed integer programming model is formulated for the bi-level joint decision in the leader-follower game. To solve the nonlinear optimization model, a bi-level, nested genetic algorithm with constraint reasoning is developed and implemented. A case study of a power transformer product family and supply chain configuration is reported to demonstrate the feasibility and potential of the proposed leader-follower game-theoretic method.

A collaborative ant colony metaheuristic for distributed multi-level uncapacitated lot-sizing

The paper presents an ant colony optimization metaheuristic for collaborative planning. Collaborative planning is used to coordinate individual plans of self-interested decision-makers with private information in order to increase the overall benefit of the coalition. The method consists of a new search graph based on encoded solutions. Distributed and private information are integrated via voting mechanisms and via a simple but effective collaborative local search procedure. The approach is applied to a distributed variant of the multi-level lot-sizing problem and evaluated by means of 352 benchmark instances from the literature. The proposed approach clearly outperforms existing approaches on the sets of medium- and large-sized instances. While the best method in the literature so far achieves an average deviation from the best-known non-distributed solutions of 75% for the set of the largest instances, for example, the presented approach reduces the average deviation to 7%.

Exact solution approaches for bilevel lot-sizing

In this paper we propose exact solution methods for a bilevel uncapacitated lot-sizing problem with backlogs. This is an extension of the classical uncapacitated lot-sizing problem with backlogs, in which two autonomous and self-interested decision makers constitute a two-echelon supply chain. The leader buys items from the follower in order to meet external demand at lowest cost. The follower also tries to minimize its costs. Both parties may backlog. We study the leader’s problem, i.e., how to determine supply requests over time to minimize its costs in view of the possible actions of the follower. We develop two mixed-integer linear programming reformulations, as well as cutting planes to cut off feasible, but suboptimal solutions. We compare the reformulations on a series of benchmark instances.

Design and implementation of economics-based resource management system in ad hoc grid

The ad hoc grid is a spontaneous organization of cooperating heterogeneous nodes into a logical community without a fixed infrastructure and with only minimal administrative requirements. Resource management for ad hoc grids is challenging due to the participation of heterogeneous, dynamic, autonomous and ephemeral grid nodes. The paper proposes an ad hoc grid resource management system, the producers and consumers of ad hoc grid resource are modeled as the self-interested decision-makers described in microeconomic theory. All market participants in the ad hoc grid environment including grid resources and services can be represented as agents. We apply economic agents to build ad hoc grid resource management, where ad hoc grid resource consumers and providers can buy and sell ad hoc grid resource based on an underlying economic architecture. The main processes involved in ad hoc grid resource management are resource registration, discovery, and resource allocation. The experiments are conducted to compare ad hoc grid resource allocation algorithm with other ad hoc grid resource allocation algorithm. Simulation results show that our proposed algorithm is more efficient than compared allocation scheme.

Economic agent-based resource management in ad hoc grid environment

The ad hoc grid is a spontaneous organisation of cooperating heterogeneous nodes into a logical community without a fixed infrastructure and with only minimal administrative requirements. Resource management for ad hoc grids is challenging due to the participation of heterogeneous, dynamic, autonomous and ephemeral grid nodes. The paper proposes an ad hoc grid resource management system, the producers and consumers of ad hoc grid resource are modelled as the self-interested decision-makers described in microeconomic theory. All market participants in the ad hoc grid environment including grid resources and services can be represented as agents. We apply economic agents to build ad hoc grid resource management, where ad hoc grid resource consumers and providers can buy and sell ad hoc grid resource based on an underlying economic architecture. The main processes involved in ad hoc grid resource management are resource registration, discovery and resource allocation. The experiments are conducted to compare ad hoc grid resource allocation algorithm with other ad hoc grid resource allocation algorithm. Simulation results show that our proposed algorithm is more efficient than compared allocation scheme.

A generic coordination mechanism for lot-sizing in supply chains

A new generic mechanism to coordinate decentral planning of a group of independent and self-interested decision makers, who are searching for an agreeable contract regarding multiple interdependent issues, in the case of asymmetric information is presented. The basic idea of the mechanism is that the group members cooperatively carry out an evolutionary search in the contract space. Therefore the (1,?)-selection procedure, which is used in many evolutionary strategies, is combined with the Borda maximin voting rule, which has been applied successfully in group decision making. The proposed mechanism is realized, applied and evaluated for production coordination in a supply chain. A decentralized variant of the multi-level uncapacitated lot-sizing problem (MLULSP) is taken as the production model. For the evaluation 95 problem instances are generated based on MLULSP instances taken from the literature, with problem sizes varying from 5 to 500 items, from 12 to 52 periods. Experimental results show that the proposed mechanism is effective to determine fair cost distributions.

Distributive Justice and Infertility Treatment in Canada

An exploration of distributive justice in Canadian infertility treatment requires the integration of ethical, clinical, and economic principles. In 1971, American philosopher John Rawls proposed a theoretical model for fair decision-making in which “rational” and “self-interested” citizens are behind a “veil of ignorance” with respect to both their own position and the position of other decision-makers. Rawls proposed that these self-interested decision-makers, fearing that they are among the least advantaged persons who could be affected by the decision, will agree only upon rules that encode equality of opportunity and that bestow the greatest benefit on the least advantaged citizens. Regarding health policy decision-making, Rawls’ model is best illustrated by Canadian philosopher Warren Bourgeois in his panel of “volunteers.” These rational and self-interested volunteers receive an amnestic drug that renders them unaware of their health, social, and financial position, but they know that they are representative of diverse spheres of citizens whose well-being will be affected by their decision. After describing fair decision-making, Bourgeois considers the lack of a distributive justice imperative in Canada’s Assisted Human Reproduction Act, in contrast to legislation in European nations and Australia, summarizes the economic and clinical considerations that must be provided to the decision-makers behind the “veil of ignorance” for fair decisions to occur, and considers altruism in relation to equality of access. He concludes by noting that among countries with legislation governing assisted reproduction Canada is alone in having legislation that is void of distributive justice in providing access to clinically appropriate infertility care.

Autonomous Vehicle-Target Assignment: A Game-Theoretical Formulation

We consider an autonomous vehicle-target assignment problem where a group of vehicles are expected to optimally assign themselves to a set of targets. We introduce a game-theoretical formulation of the problem in which the vehicles are viewed as self-interested decision makers. Thus, we seek the optimization of a global utility function through autonomous vehicles that are capable of making individually rational decisions to optimize their own utility functions. The first important aspect of the problem is to choose the utility functions of the vehicles in such a way that the objectives of the vehicles are localized to each vehicle yet aligned with a global utility function. The second important aspect of the problem is to equip the vehicles with an appropriate negotiation mechanism by which each vehicle pursues the optimization of its own utility function. We present several design procedures and accompanying caveats for vehicle utility design. We present two new negotiation mechanisms, namely, “generalized regret monitoring with fading memory and inertia” and “selective spatial adaptive play,” and provide accompanying proofs of their convergence. Finally, we present simulations that illustrate how vehicle negotiations can consistently lead to near-optimal assignments provided that the utilities of the vehicles are designed appropriately.

Market Led Industrialization and Globalization

The paper introduces asymmetric production conditions between firms and asymmetric transaction conditions between countries into the Murphy-Shleifer-Vishny model of industrialization. It explores a general equilibrium mechanism that generates circular causation loop that each firm`s profitability and its decision of involvement in a network of industrial linkages is determined by the size of the network, while the network size is in turn determined by all firms` decisions of participation. It shows that the very function of the market is to network relevant self-interested decision-makers and to utilize the network effects of industrialization, though this function is not perfect. Hence, market led industrialization will gradually spread until the whole world economy is integrated in a single network of trade and industrial linkages as trading efficiency is improved. This paper devises a new approach to specifying zero profit condition for a marginal modern firm, while keeping original feedback loop between positive profit and the extent of the market of the MSV model. Hence, this new method and the trade off between economies of scale and transaction costs can be used to endogenize the number of modern sectors, thereby increasing applicability of this type of models.

Character as a Safeguard for Journalists Using Case-Based Ethical Reasoning

As suggested by D. E. Boeyink, casuistry is a promising method for making ethical decisions in journalism because its case-oriented strategy fits (the) general approach of many journalists while its stress on consistency guards against arbitrariness. Despite its emphasis on consistency, however, casuistry gives self-interested decision makers enough wiggle room to rationalize whatever is expedient. For this reason, casuistry relies also on character. Yet writers who have studied casuistry have said relatively little about the link between character and casuistry and, when they have, they have focused on the intellectual virtue of phronesis. This article articulates the essential moral virtues necessary to prevent arbitrariness in casuistry when practiced by journalists and demonstrates their relevance in relation to a recent case in which the journalist's character was a key factor. The article concludes with several strategies for nurturing good character among journalists

Electric industry governance: Reconciling competitive power markets and the physics of complex transmission interconnections

Creating efficient, competitive power markets in an electric industry composed of interconnected control areas requires the existence of some agency with authority to define, impose and enforce rules for the operation of all control areas so interconnected. It has been noted that “the pursuit of self-interest, unrestrained by suitable institutions, carries no guarantee of anything except chaos”. In no part of the economy is this lesson more relevant than in the North American electric industry. As the industry evolves from one dominated by vertically-integrated utilities into one with competitive power markets and unregulated generators, the system of coordinating institutions that has worked acceptably well to restrain and guide self-interested decision makers of economically regulated firms must now be reconstructed to restrain and guide self-interested decision makers of unregulated generating companies (gencos), power merchants and brokers.

NON-COOPERATIVE STRATEGIES FOR DYNAMIC POLICY GAMES AND THE PROBLEM OF TIME INCONSISTENCY

COMPETITION between decision makers is often characterised in economics as a noncooperative game. But when the decisions of a finite number of agents are linked over time in a multiperiod problem, as typically happens in policy models or market models of disequilibrium behaviour, the conventional solutions become dynamically inconsistent because they fail to allow for the noncausal effects, on current decisions, of rational expectations of the endogenised future choices. This has left the economist with the problem of determining what decision rule is best, and the decision maker with the problem of how to justify actions which are predictably inconsistent dynamically as well as suboptimal (Buiter (1981)). Many of the controversies of economic theory have been concerned with the behaviour of a small number of self-interested decision makers, or with economic behaviour when the assumptions of perfect competition are not appropriate. For example, Keynesian demand analysis recognises that government action can systematically influence the income level; but the monetarists doubt this, at least for the long term. The Philips curve suggests that organised labour may be able to influence the wage-unemployment trade-off; and so on. Then collective bargaining, international trade policy, or oligopolistic firms, provide further examples.' But the literature has yet to provide a detailed structural analysis of the optimal strategies for such dynamic policy games. This paper derives the optimal strategies for a two player nonzero sum dynamic game. Starr and Ho (1969) have used numerical examples to show that conventional recursive optimisation techniques (e.g. dynamic programming) can produce suboptimal decisions. By using a different optimisation method we find that dynamic programming generally produces suboptimal decisions because this kind of constrained optimisation problem lacks the time-recursive structure necessary for recursive optimisations. Hence the 'noncausal' effects (defined below, in Section 5.2) are ignored. These results are used to assess the impacts of the associated suboptimality on competitive decisions. It appears that strategies which ignore noncausal effects can be inferior even to those which ignore the game or misspecify the equilibrium conditions.