KNOWLEDGE SHARING AND ORGANIZATIONAL

PERFORMANCE

An Agent-mediated Approach

Virginia Dignum

Department of Information & Computing Sciences, Utrecht University, The Netherlands

Keywords:

Knowledge Management, Organizational Performance, Multi-Agent Systems, Agent-based Simulation.

Abstract:

Organizational effectiveness depends on many factors, including excellence, effective planning and capability

to understand and match context requirements. Moreover, organizational performance cannot be just eval-

uated in economic or other global terms, but it must consider values of the participating agents (people or

groups), such as individual satisfaction. Different organizational structures are clearly better matched to cer-

tain problems and context requirements than others, but evaluation methods are mostly lacking. In this paper,

we will present ongoing work on tools and formalisms to model organizations and evaluate their performance

according to global and individual values, under different circumstances.

1 INTRODUCTION

Today, collaboration is the common place for organi-

zational activity. Both within as across organizations,

collaborative work happens for product development,

marketing, sales, R&D, etc. For instance, in the ﬁeld

of product development several companies collabo-

rate nowadays to develop new products, e.g., cars, air-

crafts or machinery, resulting in what is referred to as

a collaborative supply chain. Collaborative develop-

ment efforts are usually conducted on a project basis,

i.e., centered around a new product or service. The

duration of such collaborative development projects

can vary from some months to several years.

Several challenges arise in such collaborative

projects. On the one hand, efﬁcient organization

structures need be implemented that facilitate the per-

formance of project teams. Typically, such teams con-

sist of employees from the different groups involved

in the collaboration, possibly augmented with exter-

nal consultants. This means that members of the team

fulﬁl a speciﬁc role on the project, but are also part,

and must answer to, the original organizational struc-

tures of their own companies and groups. Further-

more, from a knowledge management perspective,

these collaborative teams depend on knowledge that

is spread over different companies that are part of the

chain. That is, the team will need to tap in to knowl-

edge from other people in the different partner orga-

nizations. Managing this knowledge effectively con-

tributes to the performance of the supply chain as well

as the individual companies.

Effectiveness of organizations and teams depends

on many factors, including excellence, effective plan-

ning and capability to understand and match context

requirements. Despite a large number of studies, the

effect of organizational structure on the performance

and the knowledge sharing capabilities of a group

is still not well understood. Different organizational

structures are clearly better matched to certain prob-

lems and context requirements than others, but evalu-

ation methods are mostly lacking.Furthermore, envi-

ronments are not static, which implies that structuring

decisions must be adaptable to change and therefore

the process of determine congruence between struc-

ture and environment is cyclic.

Moreover, organizational performance cannot be

just evaluated in economic or other global terms, but it

must consider values of the participating agents (peo-

ple or groups), such as individual satisfaction. Intel-

ligent agents have been deﬁned as autonomous, reac-

tive, proactive entities capable of social interactions in

dynamic environments. As such, the agent paradigm

is particularly suitable to model project teams. In or-

der to cope with the inherent complexity of knowl-

edge sharing in teams, the concept of Agent-mediated

Knowledge Management (AMKM) proposes agent-

based approaches to deal with collective aspects of

the domain in an attempt to cope with the conﬂict be-

tween desired order and actual behavior in dynamic

504

Dignum V. (2007).

KNOWLEDGE SHARING AND ORGANIZATIONAL PERFORMANCE - An Agent-mediated Approach.

In Proceedings of the Ninth International Conference on Enterprise Information Systems, pages 504-509

 SciTePress

environments (van Elst et al., 2004).

In this paper, we will present ongoing work on

tools and formalisms to model organizations and eval-

uate their performance under different circumstances.

The aims of this project are to generate tools and

methods to understand and model the relation be-

tween organizational design, agent behavior and team

performance. Results will be applied to the design

of more effective agent systems that support the ac-

tivity of virtual groups. The paper is organized as

follows: in section 2 describes our work on organi-

zational models, section 3 discusses the factors that

inﬂuence performance and how to dynamically adapt

these factors in order to improve performance. In sec-

tion 4 we introduce our current work on the develop-

ment of simulations that enable to study the perfor-

mance model proposed. Finally, section 5 presents

our conclusions and directions for future work.

2 AGENT ORGANIZATIONS

The agent paradigm offers an effective way to model

and analyze complex systems composed of multiple

and distinct components. In this sense, an organiza-

tion can be seen as a set of agents whose interactions

are regulated by mechanisms of social order and are

created to achieve common goals. Furthermore, vir-

tual teams as described in the previous section, op-

erate in open environments, where autonomous par-

ticipants and stakeholders are not centrally organized

and follow heterogeneous models and motivations,

and act according to their own plans and norms.

Such open environments demand organizational mod-

els that integrate the realization of organizational re-

quirements and objectives, and at the same time al-

low participants to have the freedom to act according

to their own agendas and goals. This means that we

should take a distributed view on Knowledge Man-

agement (KM): knowledge is autonomously managed

where it is created and used, namely within each com-

munity or project team. Moreover, autonomy without

coordination is almost useless for knowledge sharing

in complex organizations, yet coordination should be

reached through interoperation rather than centraliza-

tion (Bonifacio et al., 2001). Recently, several agent-

oriented analysis methodologies for Knowledge Man-

agement have been proposed (van Elst et al., 2004;

Dignum et al., 2004).

Our approach prescribes such an agent-oriented

framework, which considers individual and social

goals in order to determine which structure is the best

applicable for a project team, given the task environ-

ment, the knowledge needs, and the organizational

constraints of its members. The framework is de-

scribed in more detail in the remainder of this section

2.1 Organizational Structure

The OperA model for agent organizations, (Dignum

et al., 2004), enables the speciﬁcation of organiza-

tional requirements and objectives, and at the same

time allows participants to have the freedom to act ac-

cording to their own capabilities and demands. OperA

considers agent organization models as having at least

two description levels. At the abstract level, which

can be seen as a receipt for collective activity, orga-

nizations are described in terms of roles, their depen-

dencies and groups, interactions and global norms and

communication requirements. The concrete level is a

possible instantiation of the abstract organization, by

populating it with real agents that play the roles and

realize interactions (Vazquez-Salceda et al., 2005),

(Sichman et al., 2005). Organizational design starts

from the identiﬁcation of business strategy, stakehold-

ers, their relationships, goals and requirements and re-

sults in a comprehensive (agent) organization model

including organizational roles, interactions and plan-

ning rules, that fulﬁl the requirements set by the busi-

ness strategy. Organizational instantiation is the pro-

cess that accepts an abstract organization model and a

set of agents, and resources and generates a concrete

organization by assigning responsibilities and organi-

zational goals to each agent.

The OperA framework consists of three interre-

lated models. The organizational structure of the so-

ciety, as intended by the organizational stakeholders,

is described in the Organizational Model (OM). The

OM speciﬁes an agent organization in terms of four

structures: social, interaction, normative and commu-

nicative. The social structure speciﬁes objectives of

the system, its roles and the model that governs co-

ordination. The interaction structure gives a partial

order of the scene scripts that specify the intended in-

teractions between roles. Interaction scene scripts are

ﬂexible constructs that describe how a result should

be achieved, instead of using procedures to describe

what are the steps to follow. Society norms and reg-

ulations are speciﬁed in the normative structure, ex-

pressed in terms of role and interaction norms. Fi-

nally, the communicative structure speciﬁes the on-

tologies for description of domain concepts and com-

munication illocutions. The way interaction occurs

in an organization depends on the aims and charac-

teristics of the application, and determines the way

roles are related to each other, and how role goals and

norms are ’passed’ between related roles. For exam-

ple, in a hierarchical organization, goals of a parent

role are shared with its children by delegation, while

in a market organization, different participants bid to

the realization of a goal of another role.

The Social Model (SM) describe how agents can

enact roles in the organizations. That is, the SM spec-

iﬁes the interaction scenes that describe the possi-

bilities for negotiation of role enactment by agents

joining the organization. These scenes generate so-

cial contracts for the participating agents that describe

the capabilities and responsibilities of an agent within

the organization, that is the desired way that an agent

will fulﬁl its role(s). The use of contracts to describe

the activity of the system allows on the one hand for

ﬂexibility in the balance between organizational aims

and agent desires and on the other hand for veriﬁca-

tion of the outcome of the system. Finally, given an

agent population for an organization, the Interaction

Model (IM) speciﬁes possible interaction protocols

between agents that implement the functionalities de-

scribed in scene scripts in the OM.

A generic methodology to analyze a given do-

main and determine the type and structure of an ap-

plication domain resulting in a OperA agent organi-

zation model is described in (Dignum et al., 2004).

The methodology provides generic facilitation and in-

teraction frameworks for agent societies that imple-

ment the functionality derived from the co-ordination

model applicable to the problem domain. Standard

organization types such as market, hierarchy and net-

work, can be used as starting point for development

and can be extended where needed and determine the

basic norms and facilitation roles necessary for the so-

ciety. A brief summary of the methodology is given

in table 1.

In order to design and analyze OperA models for

organizations, we are currently developing an graph-

ical tool, OperettA. The tool enables the veriﬁcation

of the OM, but it also generates simulations of the

organization, to be populated with agents so that its

activity can be animated.

2.2 Agents in Organizations

Section 2.1 described the modelling of organizations

from the perspective of the organization’s designer.

As such, organizational models describe the goals, re-

quirements and expectations of the organization itself.

Obviously, individual agents will have their own mo-

tivations and expectations when joining a certain or-

ganization. This implies than more than a need for

frameworks that specify the organization’s structure

and goals, we need to specify mechanisms through

which prospective participants can evaluate the char-

acteristics and objectives of society and role goals,

in order to decide about participation. Furthermore,

tools for individual agents to adapt their architecture

and functionality to the requirements of an assumed

role must be provided (Dastani et al., 2003).

In the context of project teams, organizational

roles indicate the capabilities of agents that are to ful-

ﬁl the role, typically describing the minimum expec-

tations on the enactment of participating agents. The

success, that is, improved performance, of the team

is dependent on the effort agents will put into their

role performance. As in human organizations, the

idea here is that a job description only describes part

of one’s input into the organization (as well as one’s

reward). What distinguishes employees is how well

they put themselves to their job. That is, an agent,

representing a member of one of the companies par-

ticipating in the project, is faced with a decision about

how to best enact its teams role in a way that ideally

beneﬁts both the project team as its original company.

Many issues inﬂuence this decision, including trust,

strategic reasoning and cultural characteristics.

The considerations above indicate the need to

match agent and role objectives and functionality. At

the moment most approaches to this problem sim-

ply design agents from scratch so that its behavior

complies with the behavior described by the role(s)

it will take up in the society. This applies especially

to closed MAS where all agents are designed cen-

trally and with a pre-intended purpose. In this case

the design of the agent follows from the requirements

speciﬁed for the role(s) the agent is fulﬁlling. Com-

prehensive solutions for this problem require complex

agents that are able to reason about their own objec-

tives and desires and thus decide and negotiate their

participation in a society. A ﬁrst step on the road to

this solution (taken in (Dastani et al., 2003)) is to have

a formalism to compare the speciﬁcations of agents

and roles and determine whether an agent can enact a

role. In the future, agents themselves should be able

to use this mechanism to automatically evaluate their

participation on a society.

Furthermore, once a decision has been reached

that an agent will indeed enact a role, there must be

ways to modify that agent in order to include the char-

acteristics of the assumed role. A possible solution for

this point has been proposed in (Esteva et al., 2001) in

which agents are extended with an interface to the so-

ciety. This interface prevents any action not allowed

by the role deﬁnition. However, it does not facilitate

proactive behavior expected from the agents while

playing the role. Therefore, our proposal allows for

different approaches to such modiﬁcation of an agent

which result in different role performances, by pro-

viding the for negotiation of role enactment for spe-

Table 1: Overview of OperA methodology.

Step Description Result

Coordination

Level

Identifies organization’s

main characteristics:

purpose, relation forms

Stakeholders, facilitation roles,

coordination requirements

Environment

Level

Analysis of expected

external behavior of

system

operational roles, use cases,

normative requirements

Behavior

Level

Design of internal

behavior of system

Role structure, interaction

structure, norms, roles, scripts

Population

Level

Design of enactment

negotiation protocols

Agent entrance scripts,

Role enactment contracts

Interaction

Level

Design of interaction

negotiation protocols

Scene script protocols,

Interaction contracts

ciﬁc agents in the SM. For instance, some agents will

uniquely attempt to achieve the goals of its adopted

role and forget its own private goals, while others will

only attempt to achieve the goals from the role after

its own goals have been satisﬁed.

2.3 Organizational Environment

Organizations do not exist in a vacuum. Each orga-

nization is set in a particular environment to which

it is inextricably linked. This environment provides

multiple contexts that affect the organization and its

performance, what it produces, and how it operates.

The “rules of the game” of a society are one of the

most important ingredients of the enabling environ-

ment. All societies require appropriate rules, as well

as fair and efﬁcient mechanisms by which they can

be enforced. Organizations must pursue their goals

within a normative structure that facilitates or inhibits

their work. North deﬁnes rules as “... the formal laws

and codes that positively or negatively inﬂuence the

behavior of organizations through the incentives and

constraints they provide or impose.” (North, 1990).

There are rules for all dimensions of the environment

be it at administrative, technological, political, eco-

nomic, socio-cultural, or stakeholder’s level. Rules

can be formal or informal, explicit or implicit. In ad-

dition to norms, teams and organizations possess a

certain combination of resources that inﬂuences the

type and scale of activities undertaken by the group,

as well as how successful their efforts are likely to be.

Capabilities of a group include natural resources, hu-

man resources, ﬁnancial resources, infrastructure and

technology. Together with norms, these capabilities

create an enabling or inhibiting environment for orga-

nizations (Lusthaus, 2002).

Furthermore, given dynamic environments, teams,

and therefore, their members, should have a range of

capabilities and the ability to decide how best to apply

those capabilities to changing situations. This implies

that,ideally, team members should have a sense of re-

sponsibility towards the global objectives of the team

(which in our model we represent as social contracts

to role enactment), and the ability to assess a situa-

tion and modify its behavior to maintain or improve

overall team performance.

Compelling as this aim may be, such agents are

usually quite expensive and not always available for

any project team

. Therefore, it is important to de-

sign the structure of teams in a way that adaptation is

possible without requiring all participants to have this

type of functionality and ability.

3 UNDERSTANDING

PERFORMANCE

Efﬁciency of organizations is usually measured in

terms of performance, or the degree to which goals

are achieved. Contingency theory shows that: (a)

there are no one optimal organizational design, and

(b) structural constraints, task constraints, cognitive

constraints and other environment conditions inﬂu-

ence organizational outcomes. In human settings, or-

ganizational performance has been demonstrated em-

pirically to be associated with the degree of congru-

ence (or ’ﬁt’) between organizational structure and

properties of the task or environment (Donaldson,

2001). Accordingly, it is to an organizations advan-

tage to monitor the ﬁt between its structure and mis-

Note that this observation applies both to human agents

in project teams as to artiﬁcial agents in multi-agent sys-

tems. People with wide capabilities and ability to quickly

assess a situation and determine the best course of action

to take are often high educated, have a wide and long ex-

perience and therefore are expensive. Software agents with

similar possibilities are, ifat all existing extremely complex,

and therefore computationally expensive.

sion, and to alter its structure when a misﬁt is identi-

ﬁed. There is empirical evidence that high performing

organizations can discern when environmental forces

have changed the state of congruence (i.e., the good-

ness of ﬁt), thus driving changes in the strategies (e.g.,

communication patterns, back-up behaviors) that they

employ (Entin and Serfaty, 1999). In the next section,

we will discuss in more detail the factors that inﬂu-

ence performance.

From the discussion in the previous sections, it

becomes evident that organizational performance is

a complex issue that depends on many different fac-

tors. Performance, or efﬁciency, is affected by the

organization’s design, the cognitive, by information

processing capabilities of the agents, the operating

conditions, the task and the task environment faced

by the organization (Carley and Svoboda, 1996). Re-

organization can then be seen as a means to achieve

better performance, or creating a more efﬁcient orga-

nization, by changing one or more of the factors that

affect performance. From the analysis of many or-

ganizational simulation projects, some of which will

be discussed in the next sections, and inspired by So

and Durfee (So and Durfee, 1998), we classify the

factors that determine performance into three broad

classes. The ﬁrst are the structural factors, which are

the components and features of the organization (such

as roles, dependencies, constraints, norms and regu-

lations). The second are task environmental factors,

which are the components and features of the task

(such as size, time constraints, uncertainty). The third

class of factors are agent factors, which are the char-

acteristics of the individual agents concerning task ca-

pability, knowledge (including decision making and

reasoning capabilities), social awareness, etc. These

three classes of factors jointly determine the perfor-

mance of the organization.

This suggests a 3d-space in which the results of

different organizational simulations can be placed and

eventually compared. Axes in this 3-space correspond

to the groups of factors listed above. Each point in

the space represents a speciﬁc organization instance

(that is, with a given structure, task, agent capabilities

and operating conditions). For each point, the perfor-

mance can be calculated. In this perspective, reorga-

nization means a move into another point in the space

which has a better performance. In order to cope with

complexity, we are developing simulations that based

on two dimensional projections of the overall space,

which means that one set of factors is kept ﬁxed in that

particular simulation. In the following we introduce

our work on organizational simulations along these

lines.

4 AGENT ORGANIZATION

SIMULATION

Both in social sciences as in computer science many

simulation studies have been realized that aim to in-

crease the understanding of reorganization in human

and agent organizations respectively. Different or-

ganizational simulation projects have studied the in-

ﬂuence of different factors on the organizational efﬁ-

ciency, using different techniques and aiming at prov-

ing different hypothesis.

So far, we have discussed models for the speciﬁ-

cation of organizations that enable the representation

of organizational requirements, in terms of structures

and objectives, independently from the speciﬁcation

of the individual agents. Such models enable the an-

alyze of sharing and interaction in organizational set-

tings. The novelty of the approach is that the same

model enables analyze at both the level of organiza-

tional performance as well as at the level of the indi-

vidual expectations. In this section, we will discuss

our current research towards simulation frameworks

for such models.

Currently, several agent-based simulation lan-

guages and platforms are available (e.g. Repast

By following an agent-oriented approach, these plat-

forms enable the observation and analysis of (com-

plex) social processes between the entities. However,

most of these simulations are based on very simple

models of agency, that hardly enable the speciﬁca-

tion of the cognitive aspects of the agents. On the

other hand, languages and tools for agent develop-

ment, such as 3APL, Jason or Jack, that are based on a

cognitive notion of agency (typically the BDI model)

often lack the constructs to represent high level social

aspects, such as roles, norms or organizational struc-

tures. Moreover, from a computational point of view,

these agent platforms do not provide the scheduling

and timing mechanisms required to run simulations.

A few languages attempt to counter these problems.

A good example is Brahms (Sierhuis et al., 2003), or

the MAS-SOC platform (Bordini et al., 2005).

Simulation of organizational or group behavior

requires more than the organizational model and its

members, as developed in the OperA model. The sim-

ulation must be able to incorporate the environment

conditions under which activity will take place. Sim-

ulation tools are well suitable to describe and mod-

ify environment conditions by enabling for instance to

vary task complexity and frequency, or the harshness

or the environment. Our approach is use an existing

simulation platform (Repast) and create a plugin to

http://repast.sourceforge.net/

cognitive agents developed in an agent-oriented lan-

guage (for the moment we are using 3APL

. The aim

is to eventually be able to integrate in one platform

agents developed using different languages so that

different approaches to cognition can be integrated.

Our current research on the development of a sim-

ulation tool for reorganization experimentation aims

at the identiﬁcation of conditions and requirements

for change, ways to dynamically incorporate changes

in systems, how to determine when and what change

is needed, and how to communicate about changes.

In (Dignum et al., 2006) we presented a simulation

scenario, VILLA, to evaluate different reorganization

forms and understand triggers for reorganization.We

are currently working on a simulation that analyzes

the relation between agent capabilities and organiza-

tional structure, when the objective is to share knowl-

edge on a particular task.

5 CONCLUSIONS

In this paper, we argued that effectiveness of project

teams is dependent on the structure and norms of the

team, on the capabilities and values of participants,

on the way the team is able to consider and relate to

the external links of its members, and on the over-

all characteristics of the environment where the team

exists. This means that team performance cannot be

evaluated just on economic terms but must consider

the values and expectations of participants. We have

presented a model for organization design that de-

scribes requirements concerning the overall goals and

norms of the organization or team, but also takes in

account the autonomy of individual participants. In

order to understand relation between structure and

performance of teams we are developing simulations

tools that enable the controlled variation of factors

and their effects. Our current and future work is cen-

tered around the further development of simulation

and evaluation tools.

ACKNOWLEDGEMENTS

This research is funded by the Netherlands Organi-

zation for Scientiﬁc Research (NWO), through Veni-

grant 639.021.509. The author is grateful to Christi-

aan Tick for his work on the development of the sim-

ulation environment.

http://www.cs.uu.nl/3apl/

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