Table Of ContentNEGOTIATION PROCESSES:
MODELING FRAMEWORKS AND INFORMATION TECHNOLOGY
edited by
Melvin F. Shakun
Stern School of Business
New York University
KLUWER ACADEMIC PUBLISHERS
BostonlDordrecht/London
Group Decision
and
Negotiation
Volume 5, Nos. 4, 5, 6, September-December 1996
Special Issue on Negotiation Processes: Modeling Frameworks and Information Technology
Special Editor: Melvin F. Shakun
Introduction ............................................... Melvin F Shakun
Modeling and Supporting Task-Oriented Group Processes: Purposeful Complex
Adaptive Systems and Evolutionary Systems Design .............. Melvin F Shakun 5
Using a Group Support System to Implement Evolutionary Systems Design
· ....................................... L. Floyd Lewis and Melvin F Shakun 19
Negotiation Processes, Evolutionary Systems Design, and
NEGOTIATOR .............................. Tung X Bui and Melvin F Shakun 39
Game Theory Practice ...................................... Kalyan Chatterjee 55
Negotiation Support Using the Decision Support System GMCR .... D. Marc Kilgour,
Liping Fang, and Keith W. Hipel 71
Negotiation and Design: Supporting Resource Allocation Decisions through Analytical
Mediation ............................. Jeryl L. Mumpower and John Rohrbaugh 85
A Prototype NSS Based on Problem Structure and Suggestions Toward More
Comprehensive Negotiation Support ............. Timothy R. Hill and Beth H. Jones III
Perspectives on Representation and Analysis of Negotiation: Towards Cognitive Support
Systems ................................... Gregory E. Kersten and David Cray 133
Computer-Supported Cooperative Negotiations ................. Stephen J. Andriole 169
On-Line Group Decision Support by Preference Programming in Traffic Planning
· ................................... Raimo P Hamalainen and Mari P6yh6nen 185
"Horses for Courses"-A Stakeholder Approach to the Evaluation of GDSSs
· ........................................... Colin Eden and Fran Ackermann 201
A Research Framework for Group Support Systems ......... Caroline A. Stevens and
Paul N Finlay 221
Distributors for North America:
Kluwer Academic Publishers
101 Philip Drive
Assinippi Park
Norwell, Massachusetts 02061 USA
Distributors for all other countries:
Kluwer Academic Publishers Group
Distribution Centre
Post Office Box 322
3300 AH Dordrecht, THE NETHERLANDS
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available
from the Library of Congress.
ISBN-13: 978-94-010-7313-4 e-ISBN-13: 978-94-009-1824-5
DOl: 10.1007/978-94-009-1824-5
Copyright © 1996 by Kluwer Academic Publishers
Softcover reprint of the hardcover 1s t edition 1996
All rights reserved. No part of this publication may be reproduced, stored in
a retrieval system or transmitted in any form or by any means, mechanical,
photo-copying, recording, or otherwise, without the prior written permission of
the publisher, Kluwer Academic Publishers, 101 Philip Drive, Assinippi Park,
Norwell, Massachusetts 02061
Printed on acidjree paper.
Group Decision and Negotiation, 5:301-304 (1996)
© 1996 Kluwer Academic Publishers
Introduction to the Special Issue on Negotiation
Processes: Modeling Frameworks and Information
Technology
MELVIN F. SHAKUN
Leonard N Stern School of Business, New York University, 44 West 4 Street, New York, NY 10012-1126
The INFORMS (formerly TIMS) Section on Group Decision and Negotiation sponsored
a Negotiation Writing Project on the theme, "Negotiation Processes: Modeling Frame
works and Information Technology," leading to a group of sessions at the TIMS/ORSA
Boston meeting, April 24-27, 1994. The articles published here derive from this project.
The idea of the writing project was to discuss how negotiation modeling frameworks
and information technology can support negotiation processes. Negotiation may be de
fined as "a process of potentially opportunistic interaction by which two or more parties,
with some apparent conflict, seek to do better through jointly decided action than they
could otherwise" (Lax and Sabenius 1986, p. 11). These authors note that, rather than
"opportunistic," the term "strategic" interaction in the game-theoretic sense is perhaps
preferable, meaning that "each party seeks advantage by taking the others's actions and
plans into account in deciding what to do, expecting the other to do likewise." Negotiation
involves both cooperation and conflict in a group----cooperation among the players to
create value (increase the size of the pie) and conflict to claim it (take as big a slice of the
pie as possible). In general, there are trade-offs here.
In his article, Shakun points out that negotiations are purposeful complex adaptive
processes involving multiplayer, multicriteria, ill-structured, evolving, dynamic problems
subject to self-organization, in which players both cooperate and conflict in choosing and
delivering values to participants as operational goals through decision making. Such
processes also constitute policy making. Shakun views policy making, group decision,
negotiation, and multiagent problem solving with human and/or artificial agents as
one-as task-oriented group processes that are fundamentally design processes. For such
processes, he develops Evolutionary Systems Design (ESD) as a general formal modeling
framework that can be applied to define and solve specific problems. The ESD framework
is an evolving group problem representation or structure defining relations between sets of
elements (players, values, goals, controls, etc.) in task-oriented group processes. The ESD
framework can be implemented by computer group/negotiation support systems. 1
The next two articles by Lewis and Shakun and by Bui and Shakun involve computer
implementation of ESD. Lewis and Shakun describe a general purpose group support
system (GSS), MeetingWorks for Windows, that provides a set of software tools (generate,
organize, cross-impact, etc.) for group meeting support. Using MeetingWorks, the authors
discuss how a group with the help of a facilitator trained to run electronic meetings may
302 SHAKUN
create and execute a meeting script (detailed agenda or sequence of MeetingWorks tools)
to implement the ESD problem representation. Bui and Shakun discuss how a negotiation
support system (NSS), NEGOTIATOR, can implement ESD in generating individual
preferences (utility functions), restructuring the negotiation problem, and proposing com
promise solutions.
In his contribution, Chatterjee reviews recent research themes in game theory, empha
sizing noncooperative games in extensive form. He concludes that games theory does not
furnish answers, i.e., precise advice for individual negotiators, or predictions of negotia
tion behavior and outcomes. Rather game theory is valuable in providing useful qualitative
insights and nuanced contingent explanations regarding the strategic interaction that char
acterizes negotiation. Chatterjee's work sheds light on the contribution of game theory to
negotiation practice.
Also game-theoretic in character, the article by Kilgour, Fang, and Hipel utilizes the
Graph Model for Conflict Resolution that is computer implemented in the negotiation
support system, GMCR. Decision makers and their options are identified. A combination
of options selected by decision makers constitutes a state with regard to which each
decision maker has a preference ranking (ordinal payoff). Reachable lists show for each
given state and each decision maker the set of states which that decision maker can
achieve, unilaterally and in one step, starting from that given state. The graph model
depicts such unilateral movements in the conflict. Each vertex represents one of the states.
An arc shows each feasible movement between two states and the identity of the decision
maker controlling the movement. GMCR calculates whether each state is stable or un
stable for each decision maker under each of a variety of stability solution concepts
(stability types). When a state is stable for all decision makers, it constitutes a possible
resolution or equilibrium under that stability type. The authors apply their model to an
illustrative case involving environmental negotiations, in which they trace the evolution of
the conflict.
Mumpower and Rohrbaugh view negotiation as a design process involving resource
allocation. Negotiation typically involves a mix of consonant objectives (as in budgeting
problems where negotiators agree that more is better) and discordant objectives (as in
contract problems where, for a given issue, negotiators are in opposition regarding
whether more is better or less is better). The authors define the structure of an negotiation
as the joint distribution of negotiators' utilities across all possible combinations of re
source allocations. This joint distribution defines the feasible space and the efficient
frontier which depend on the negotiators' utility functions and the level of available
resources. Using this framework, the authors suggest computer-supported analytical me
diation in which a neutral third party assists negotiators in reaching solutions, e.g., by
proposing potential agreements. Mediation makes use of different approaches (e.g., com
promise, logrolling), depending on the consonant/discordant nature of the negotiation as
depicted in the joint utility space. The authors illustrate with two cases involving a budget
problem (consonant) and a labor-management contract problem (discordant).
As in the preceding article Hill and Jones also make use of negotiation problem
structure defined by the joint utility distribution. They present a prototype NSS-mouse
driven with color graphics and animation-that allows the user to visualize hypothesized
2
INTRODUCTION TO THE SPECIAL ISSUE ... 303
problem structures and specific contracts, points in joint utility space, facilitating "navi
gation" of this space. Based on preliminary experimentation with the prototype NSS, the
authors suggest more comprehensive NSS support based on a negotiation process formal
ism (SANP), Cognitive Action Theory, neural network technology, and computer simula
tion.
Kersten and Cray discuss cognitive support systems, NSS emphasizing (1) negotiator's
needs, cognitive facility, and instrumental support; (2) organizational setting (agent, other
agents, environment); and (3) decision making involving intuition, analogy, and generali
zation. The authors offer restructurable modeling as a negotiation framework and basis for
cognitive support systems. Restructurable modeling accommodates an evolving problem
representation-characteristic of negotiation-involving problem adaptation and restruc
turing. The authors have implemented restructurable modeling in their rule-based Ne
goplan system, an evolving cognitive support system.
Andriole looks at cooperative/collaborative computing, electronic publishing, intelli
gent systems, networked communications, and computer-based analytical methodology
all supported by a network of processors, applications, and databases. He explores how
these might be applied in negotiation. Based on a process-task model of negotiation, he
matches information technologies with negotiation tasks, while at the same time propos
ing an agenda for experimental research.
Hiimiiliiinen and Poyhonen develop on-line group decision/negotiation support using
the preference programming multiple criteria decision making (MCDM) technique. With
preference programming, a common group interval model of preferences is constructed,
and negotiation evolves by decreasing the width of the intervals. The authors successfully
apply the preference programming approach to a realistic traffic planning problem using
the HIPRE 3+ Group Link software. No clear differences were found between two
working procedures, one starting with individual preference models from which a com
mon group interval model is constructed, and the other starting with a common group
interval model which does not originally include any preference statements, i.e., the
intervals are first as wide as possible.
Eden and Ackermann develop a stakeholder approach to the evaluation of group
decision/negotiation support systems. Stakeholders include users (the authors distinguish
between clients, key actors, group members, and the sponsoring organization), facilitators,
developers, vendors, and academics. The authors develop evaluation criteria for the vari
ous stakeholders in hierarchical outline (tree) form. They suggest that a data envelopment
analysis (DEA) or suitable MCDM method could be applied for formal evaluation.
In the last article Stevens and Finlay propose a research framework for group/
negotiation support systems involving context, process, and outcome variables. Six major
components are identified: organizational environment, group context, process context,
group process, substantive outcomes, and process performance indicators. Within these
six components, 16 macrovariables are identified. Within these macrovariables, 90 vari
ables are selected. The framework can be used in examining past research, in generating
hypotheses, and in designing and evaluating future group/negotiation support systems.
In closing, we note that many of the articles are directly relatable to the ESD general
formal modeling framework and its computer implementation in defining relations be-
3
304 SHAKUN
tween sets of elements in task-oriented group processes. Thus, ESD can be viewed as
providing a framework for relating articles in this volume.
Notes
I. The terms "group support systems" (GSS) and "negotiation support systems" (NSS) are sometimes used to
emphasize support for different aspects of task-oriented group processes.
References
Lax, D. A., and J. K. Sebenius. (1986). The Managerial Negotiator: Bargainingfor Cooperation and Competitive
Gain. New York: Free Press.
4
Group Decision and Negotiation, 5:305-317 (1996)
© 1996 Kluwer Academic Publishers
Modeling and Supporting Task-Oriented Group
Processes: Purposeful Complex Adaptive Systems and
Evolutionary Systems Design
MELVIN F. SHAKUN
Leonard N. Stern School of Business, New York University, 44 West 4th Street, New York, NY 10012-1126
Abstract
Evolutionary Systems Design (ESD) is a universal general problem solving, formal modeling, design framework
for purposeful complex adaptive systems (peAS) and processes, i.e., task-oriented group processes. These
processes constitute policy making, group decision, negotiation, and multiagent problem solving with human
and/or artificial agents. ESD is also a framework for computer group support systems (GSS) that support these
processes. The ESD general framework can be applied to define and solve specific problems. In this article the
ESD framework is presented and illustrated by example. The article provides background for ESD computer
implementations discussed in two other related articles (Lewis and Shakun 1996; Bui and Shakun 1996).
Key words: purposeful complex adaptive systems and processes, Evolutionary Systems Design, task-oriented
group processes, policy making, group decision, negotiation, multiagent problem solving with human and/or
artificial agents, Group Support Systems
1. Purposeful complex adaptive systems and Evolutionary Systems Design
Purposeful complex adaptive systems (peAS) and processes (their time descriptions as
dynamical systems) involve multiplayer, multicriteria, ill-structured, evolving, dynamic
problems subject to self-organization in which players both cooperate and conflict in
choosing and delivering values to participants as operational goals through decision mak
ing. Thus, such systems are both cybernetic and self-organizing. These are purposeful
complex adaptive systems 1. Task -oriented groups, organizations and economies are
peAS. Here we focus on task-oriented groups. Processes exhibited by such groups con
stitute policy making viewed as design of peAS. These processes also constitute nego
tiation, "a process of potentially opportunistic interaction2 by which two or more parties,
with some apparent conflict, seek to do better through jointly decided action than they
could otherwise" (Lax and Sebenius 1986, p. 11). Negotiation involves both cooperation
and conflict in a group-cooperation among the players to create value (increase the size
of the pie), and conflict to claim it (take as big a slice of the pie as possible). In general,
there are trade-offs here.
From the perspective of purposeful complex adaptive processes, we view policy mak
ing, group decision, negotiation, and multi agent problem solving with human and/or
5
306 SHAKUN
artificial agents as one, as mutually explanatory metaphors all referring to what happens
in such processes.
Evolutionary Systems Design (ESD) (Shakun 1988, 1990, 1991, 1992, 1993) is a meth
odology-a universal general problem solving, formal modeling, design framework3 -for
purposeful complex adaptive systems (peAS) and processes, i.e., task-oriented group pro
cesses. ESD is also a framework for group support systems (GSS) that support these
processes (see Figure 1). GSS based on ESD can provide support for individual players and
for the group.4 The ESD general framework can be applied to define and solve specific
problems. The ESD framework (problem representation or structure) may be expressed as
two evolving hierarchies involving relations between the following sets of elements: (1)
players, agents, negotiators or decision makers in a group (coalition); (2) values or broadly
stated desires; (3) operational goals, or concrete expressions of these values; (4) decisions,
Evolutionary Systems Design (ESD)
Purposeful Complex Adaptive Processes Group Support Systems
Task-Oriented Group Processes (GSS)
Policy Making
Group Decision
Negotiation
Multiagent Problem Solving
Figure 1. Modeling and supporting purposeful complex adaptive processes-task-oriented group processes.
6
MODELING AND SUPPORTING TASK-ORIENTED GROUP PROCESSES 307
actions, or controls taken to achieve these goals; (5) criteria based on goals for evaluating
the effectiveness of decisions; (6) individual preferences defined on criteria; and (7) group
or coalition preference defined on individual preferences.
More specifically, players or agents may be human or artificial. They may be multicul
tural. They are members of a group engaged in collective decision making. Values are
beliefs regarding desired or preferred modes of conduct (instrumental values) or end
states of existence (terminal values) (see Rokeach (1973)). For example, Maslow's (1954)
values hierarchy, involving safety, security, love, self-esteem, and self-actualization, ex
presses terminal values. Values and goals represent wants. Operational goals are beliefs
defined by specific, unambiguous operations and are characterized by performance mea
sures. They are operational expressions of higher level values. Goals are delivered by
controls (decisions or actions) chosen by players. Goals are used as criteria for evaluating
the effectiveness of decisions. When goals are risky, criteria such as means, standard
deviations, and so on, can be defined on probabilistic goal outputs. Individual preferences
and group or coalition preference are aids in finding solutions.
The relations between the sets of elements can be represented by two evolving hierar
chies of relations.s These hierarchies may be in the form of trees or other graphs. The
hierarchy 1 relation (see Figure 2) is a framework for evolving (i.e., for defining) the
general problem in the sense of defining values to be delivered to group members in the
form of operational goal variables by exercising control variables. Hierarchy 1 is con
cerned with what Fisher, Ury, and Patton (1991) call interests. The hierarchy 2 relation
(see Figure 3) is a framework for finding a solution-finding the levels or particular values
of the control and goal variables-to the evolved general problem at any stage. Hierarchy
2 is concerned with what Fisher, Ury and Patton call positions. Together, hierarchies 1 and
Values
Goals/values relation
Goal Variables
Controls/goals relation
Control (decision) Variables
Figure 2. Hierarchy I relation between control variables, goal variables, and values.
7
