Concise Interactions and Effective Management of Shared Design Spaces: Moving Beyond Strategic Collaboration Towards Co-Design

Abstract

Often, design problems are coupled and their concurrent resolution by interacting stakeholders is required. The ensuing interactions are characterized predominantly by degree of interdependence and level of cooperation. Since tradeoffs, made within and among sub-systems, inherently contribute to system level performance, bridging the associated gaps is crucial. With this in mind, effective collaboration, centered on continued communication, concise coordination, and non-biased achievement of system level objectives, is becoming increasingly important. Thus far, research in distributed and decentralized decision-making has focused primarily on conflict resolution. Game theoretic protocols and negotiation tactics have been used extensively as a means of making the required tradeoffs, often in a manner that emphasizes the maximization of stakeholder (personal) payoff over system level performance. More importantly, virtually all of the currently instantiated mechanisms are based upon the a priori assumption of the existence of solutions that are acceptable to all interacting parties. No explicit consideration has been given thus far to ensuring the convergence of stakeholder design activities leading up to the coupled decision and the associated determination of values for uncoupled and coupled design parameters. Consequently, unnecessary and costly iteration is likely to result from mismatched objectives. In this paper, we advocate moving beyond strategic collaboration towards co-design. We present an alternative coordination mechanism, centered on sharing key pieces of information throughout the process of determining a solution to a coupled system. Specifically, we focus on (1) establishing and assessing collaborative design spaces, (2) identifying and exploring regions of acceptable performance, and (3) preserving stakeholder dominion over design sub-system resolution throughout the duration of a given design process. The fundamental goal is to establish a consistent framework for goal-oriented collaboration that (1) more accurately represents the mechanics underlying product development and (2) facilitates interacting stakeholders in achieving their respective objectives in light of system level priorities. This is accomplished via improved utilization of shared resources and avoidance of unnecessary reductions in design freedom. Comparative performance of the proposed method is established using a simple example, involving the resolution of a tradeoff with respect to a system of non-linear equations.

Understanding Design Decisions Under Competition Using Games With Information Acquisition and a Behavioral Experiment

“…In design under competition, different players develop solutions to the same problem and the contest designer picks the best solution. On the other hand, in non-cooperative design, designers work on different aspects of the overall systems design problem with the goal of achieving Pareto optimality [10]. The literature on design for market systems (DFMS) [11] is focused on modeling manufacturing firms competing in a profit maximizing game.…”

8.1.2 A Study of Applying Game Theoretic Concepts on Distributed Engineering System Design

“…Almost all the literatures of decentralized design problems, such as (Vincent 1983, Chen and Lewis 1999, Chanron and Lewis 2003, Fernandez et al 2005, take the existence of the Nash solutions as granted and demonstrate their result only by simple examples. However, as pointed out in (Basar and Olsder 1999) in the set-valued mappings, which is equivalent to the claim that every finite strategic form game has a Nash solution.…”

“…Before we discuss the convergence of the dynamics in the decentralized system design environment, it is sensible to discuss the more fundamental question: whether the Nash solution exists in this simultaneous response framework. Almost all the literatures of decentralized design problems, such as (Vincent 1983, Chen and Lewis 1999, Chanron and Lewis 2003, Fernandez et al 2005, take the existence of the Nash solutions as granted and demonstrate their result only by simple examples. However, as pointed out in (Basar and Olsder 1999) and (Fudenberg and Tirole 1991), when i i E X = , a Nash solution is not guaranteed to exist, and, when i i M X = , the Nash solution would exist when the objective for designer i, ) , ( (Nash 1950) applied the Kakunati's fixed point theorem to prove the existence of the strategy profile ) , ( * 2 * 1 x x in the set-valued mappings, which is equivalent to the claim that every finite strategic form game has a Nash solution.…”

“…Recently, (Chen and Lewis 1999) considered the sequential response framework together with the robust design techniques to provide flexibility in a design problem, and Lewis, 2003 and2004) investigated the stability issues in this field as well. Moreover, (Fernandez et al 2005) proposed a co-design mechanism for the distributed system design problem and made a brief review of the applications of game theoretic concepts in engineering system design. In summary, the game theoretic concepts mostly serve as the communication protocol or mechanism design principles in the research literature.…”

Market-Based Strategic Platform Design for a Product Family Using a Bayesian Game

“…Xiao et al (2002) applied game theoretic approaches and design capability indices to model the relationships between engineering teams that were described as cooperative, non-cooperative, and leader/follower protocols, and facilitate collaborative decision making during a product realization process. Fernandez et al (2005) proposed a framework for establishing and managing collaborative design spaces by combining elements of cooperative and non-cooperative behavior, and formulating strategic and extensive games with utility theory. Kopin & Wilbur (2005) introduced a Bayesian game to model cost sharing in uncertain and incomplete information that were related to producer and consumer attributes such as nature, production costs, players and information, and preferences.…”