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Vulnerabilita delle Organizzazioni Umane: Modelli e Strategie
Information
Response
Decision-Making
Managerial IPK and its roles
Vulnerability of Human Organizations: Models and Strategies
An Introduction
14 Octobre 2005
Adam Maria Gadomski
http://erg4146.casaccia.enea.it
Road-map
• Objective of the work
• SoA: Human-Organizations Vulnerability
(HOV)
• Human-Organization Theory (HOT)
• HOT’s Top-Ontology
• Social and Cognitive Factors
• Critical Relations
• IPK, UMP and Role frameworks
• Pathologies and Errors
• Organization Decision-Making
• Strategies
• Intelligent Infrastructure Network
• Conclusions
Objective of the work
The research is focused on:
elaboration of models for the enabling of computer simulation for:
• prediction of events,
• early diagnosis,
• improvement and reinforcement (support)
related to h-organizations’ D-M (Decision-Making).
Vulnerability of h-organization

Vulnerability of D-M processes
Early recognition of vulnerability and, in consequence, computerbased decisional support should lead to the reduction/elimination of the
possibility of losses caused by various vulnerability of human organizations
in situation of emergency, crisis, hazards, …
SoA: Human-Organizations Vulnerability (HOV)
[FEMA]
A Vulnerability Analysis allows you to consider many different types of events, which could
have a negative impact on your people and your business.
The key to the use of the Vulnerability Analysis is the recognition of the many types of
emergencies, which could affect you, and the resources that are available to respond to the
emergency.
The State of the art
-There are rich literature about theory of organization and management science
but only few related to Human-Organizations Vulnerability
- numerous articles related to specific cases on the Web but without any theory.
- Google:
7 per "human organizational errors".
- Google: 449 per human, "organizational vulnerability".
- Google: 549 per "BUSINESS vulnerability", organization.
-"human organizational vulnerability", - non ha prodotto risultati in nessun documento.
-"human organization vulnerability", - non ha prodotto risultati in nessun documento.
-The domain is relatively new but it grows very fast.
SoA: Human-Organizations Vulnerability (HOV)
The State of the Art in the HOV modeling
Three main types of modeling approaches in the SoA:
1.
Soft modeling: descriptive, partial and intuitive – human-oriented
2.
Hard mathematico-physical modeling: partial, continuous processes,
difficulty with measurements, idealistic – for illustrative simulations,
external observer. Computer oriented.
3.
Flexible socio-cognitive modeling: computational, real-world, systemic,
AI techn., external and internal observers. For simulation and decisionsupport. Human-computer oriented. In the development.
SoA: Human-Organizations Vulnerability (HOV)
Intuitive Problem identification
Vulnerability: Lack of immunity or insufficient résistance on unexpected
events.
Two basic types of vulnerability:
A.Vulnerability on external events: dangerous situations, attacks, intrusions
B. Vulnerability on internal events: internal crisis, pathologies, reorganization.
Some observations
- Attacks, intrusions are intended to destroy key functionalities of the organisation
while fraud is designed to make money for the perpetrator [John.Bigham,2004]
Most danger are attacks from within the organisation, viz. from disaffected employees
and the software they use, or people obtaining passwords through organization
employers.
A recent survey in USA has shown that around 30-40% of attacks have their
sources within the enterprise.
SoA: Human-Organizations Vulnerability (HOV)
Some observations
A recent survey in USA has shown that around 30-40% of attacks have their
sources within the enterprise.
Therefore this attacks are most efficient and dangerous. They are human
dependent and their analysis have to involve sophisticated socio-cognitive
models.
B type vulnerability
Hannan and Freeman (1984) developed a (soft) theory of structural inertia with
two main parts. The first part claims that social selection processes favor
organizational inertia. The core insight is that social actors have the
unintended resistant to change.
According to them, two situations generate loose coupling:
- diversity of interest among members and
- uncertainty about means-end connection.
It means that organizations respond relatively slowly to threats and
opportunities in the environments. If it takes less time to build new organizations
that fit a new environment than to reorganized existing organizations.
SoA: Human-Organizations Vulnerability (HOV)
Some examples:
Hard Mathematical model of survival analysis of human organization (Cox and Oakes 1982).
Hazard related to the dead of a human organization during reorganization changes.
t
Haz(x, s, t) =  haz (x, u)du
s
x – organization indicator
s - moment of the official decision of an reorganization
t - moment of the end of reorganization
In this case, the probability of the resistance of the reorganization in moment t is (1- G(x, s, t)), where:
t
G(x, s, t) = Pr {surv(x, s, t) = 1} = exp  haz (x, u)du = exp(Haz(x, s, t))
s
 1 if ∀ u [u ∈ [s, t) → risk(x, u) = 1],
surv (x, s, t) = 
 0 otherwise.
Here, a risk is defined as a binary function risk(x, s) mapping from organizations and time
points that equals 1 if x is at risk of mortality (and, therefore, has not yet experienced mortality)
at s and equals 0 otherwise.
Unfortunately such fundamental function as haz (x, u) is not defined by the authors.
SoA: Human-Organizations Vulnerability (HOV)
Some recent examples:
Recent studies, Complexity, Science and Society Conference, 2005, Crisis Response Systems
through a Complexity Science Lens, A. Paraskevas ,Oxford Brookes University
Crisis Management is increasingly gaining importance in organizational literature
due to the precipitous growth in the total number of organizational crises over the past 4
years. The practices currently employed are largely based on a linear command-andcontrol management approach aiming at very specific results.
Managers increasingly realise “…that anytime you are not in a crisis,
you are instead in a pre-crisis mode” (Fink, 1986:5).
Complexity perspective ( in preliminaty phase): Self-awareness by Diffuse Feedback
In the absence of central control, the crisis response system must to monitor its overall
performance. Segel (2000).
Summarising, numerous studies lead to the conclusion that a main critical
aspect of vulnerabilities of human organization is a decision-making.
Human-Organization Theory (HOT)
HOT is a subtheory of TOGA
HOT is a Real-World theory, it means it has to be complete on the level
of generality of a real-world description in order to fulfill utility requirements.
Remark: Every theory is a knowledge.
Most general,
let U denotes an infinite set of the real world states, and Mx denotes a
complete model of U then Thy is a real world theory if

Thy(U)
Mx
Examples of a complete description of the U
M1: { A, B }, where A are all material objects and B are all only energy objects.
M2: {A, B, C, D }, where A are all humans, B are all their interactions, C are other
U components, D are other interactions.
Methodological soc-cog framework: TOGA
Top-down
– problem recognition & specification
Object-based – a fundamental conceptualization
Approach
Goal-oriented – problem recognition & specification
According to the current needs. TOGA will be introduced
successively and we will use TOGA’s:
- axioms…
- terminology
- generic systemic computational models
- methodology
Remark: Computationability requires a mathematical formalization.
HOT’s Top-Ontology: First comprehension level
Valid for every problem
A modeler ( M ) perspective
Human organization, 
M
Environment, 
Interactions,
R

(, R, )
Foundation Goal,
R


Human organization is an artificial system which includes human
components
Theorem
 (, .)
(.)
?
We use: G-S
interrelation
Every components of the triple (, R, ) is
decomposable, i.e. 1, 2, … R1, … 1, 2, … are
functionally, processually and structurally connected.
i, Rj,  k which
are components subsequently of : , R, .
HOT Top-Ontology: Definition of vulnerability
Human organization, 
Environment,

Interactions,
R
Domain of Activity,

States,
S
Foundation Goal,

…
Abstract objects


Possible h-organization worlds
include domains of activity 
with:
- goal-domain
Vulnerability on X , v
- cooperation domain
- intervention domain.
Vulnerability v(, X) is an attribute of , when exist such class of S(, )
which produces losses for  in the case of the R| X , where X denotes a specific
class of R charactized by a risk.
HOT Top-Ontology: Identification of vulnerability:
Identification of the vulnerability requires an identification of
objects and relations involved:
v(, X)  W (, , S(, ), R|X )
W - denotes a world of problem.
On the other hand, identification of v(, X) is necessary for
its analyzing and reducing.
Therefore we need to have a problem-independent framework
of a generic world of problem :
W (, , S(, ), R|X (r, .), T)
Such model has to be decomposed successively and
should enable to observe and simulate pathologies
of organizations which lead to organizational
erroneous decisions.
Human organization, 
Environment,

Interactions,
R
Domain of Activity,

States,
S
Foundation Goal,

…
Vulnerability on X , v
Risk
r
Observation time
Interval
T
HOT Top-Ontology: Identification of Problem World
W (, , S(, ), R|X (r, .))
Human organization, 
……… (*)
is a carrier of organizational decisional processes (ODM).
ODM is constructed on different levels of h-organization.
We need to identify such set of observable/measured
attributes (AW) which will be common for the model of W
and ODM.
Environment,

Interactions,
R
Domain of Activity,

States,
S
Foundation Goal,

…
Vulnerability on X ,
v
Risk
r
Observation time
In this case, modification of ODM will change W and will
lead to the changes of v(, X).
In order to find AW the components of the W model (*)
have to be decomposed and individually modeled.
Some separate models of the W-model components are in the
subject matter literature.
Interval
Problem world
Decisional
processes
Common Space
T
W
ODM
AW
HOT Top-Ontology: Models of the components of
W-model
We have many specific models of:
• organizations,
• their domains of activity,
• risky and losses generation events (emergency, crisis, …)
• managerial decisional mechanisms,
but they have numerous different goals, conceptualizations
(ontologies), and are not integrated/ordered for the
vulnerability modeling.
Human organization, 
Environment,

Interactions,
R
Domain of Activity,

States,
S
Foundation Goal,

…
Vulnerability on X ,
v
Risk
r
Observation time
Interval
Problem world
Anyway some critical relations between W-models
components are recognized.
Decisional
processes
Common Space
The main are:
ODM – organization structure
Individual risk – organization risks – ODM
Event types - ODM constrains
T
W
ODM
AW
Social Factors: Decomposition of the Domain
Social factors identification
They require decomposition of the organization environment.

Social factors:

a.
development/life
cycle phase;
new, old …

intervention
cooperation

1
2
n
dependence
Organization World:
decomposed objects and relations
m 
b.
structural
constrains
c.
preparedness :
proper
exercitations
d.
politic influences
e.
technological
communication
infrastructures
Cognitive Factors: Decomposition of an Organization

Cognitive organizational factors

i.
individual motivations
ii.
accepted risk
iii.
individual power and autonomy
iv.
individual recognition
Critical relations:
ODM (decision-making) – org.structure
technological support unit
human unit
Organizational unit
Critical relations: decision-making – org.structure
EXAMPLES:
1. CASE STUDY
The Collapse of Decision Making and Organizational Structure on Storm
King Mountain, T. Putnam, Ph.D. USDA Forest Service, Missoula Technology
and Development Center,1995.
2. SOFT MODEL: Restrictive Control and Information Pathologies in
Organizations, W. Scholl Journal of Social Issues, Vol.55 Issue 1, Spring 1999 :
Restrictive control is a form of power exertion in which one actor pushes
his wishes through against the interests of another actor.
In contrast, if an actor influences the other in line with his or her interests,
this is called promotive control. … (common interests).
Restrictive control has negative consequences for the production of new
or better knowledge, because it induces information pathologies that in
turn lower the effectiveness of joint action.
These two hypotheses are tested in a study on 21 successful
and 21 unsuccessful innovations.
Critical relations: intelligent object - decision-making
Organization is seen as an embedded intelligent complex
object: new cognitive, AI, socio-cognitive perspectives are
involved.
From last 15 years Studies are focused on numerous specific cases: artificial
societies, emotions, cognitive decision-making. An illustration:
- International Workshops: Engineering Societies in the Agents World,
- Periodical Workshops: "Emotion-Based Agent Architectures"
- Emotions in Humans and Artifacts, MIT Press , 2003
- Journal of Artificial Societies and Social Simulation.
- Workshops: Modelling Artificial Societies and Hybrid Organizations
- Google: 2.850.000 per high-risk, decision-making
- Google: 5.620.000 per cognitive decision-making,
Systemic Approach
IPK : TOGA Intelligent Agent Decomposition Paradigms
Intell. Object Modelling
IPK Cognitive Architecture
- Information - How situation looks
- Past/Present/Future states of
Domain-of-Activity (D-o-A)
I
- Preferences -
P
- A partial ordering of possible states of
D-o-A and they determine what is more
important
K
“ Mind Cell” Elementary
IPK Computational
Model
- Knowledge - What agent is able to associate
(descriptive/model knowledge: rules, models)
- What agent is able to do in Domain-ofActivity (operational knowledge)
Information is processed by Knowledge:
I’ = K j( I ), j=1, …N,
for l
where choice of j depends on Preferences.
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Adam M. Gadomski, 23/06/2005
IPK Cognitive Architecture
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Adam M. Gadomski, 23/06/2005
IPK: Cooperative Intelligent Objects
K
Example
P
Agent 1
I2
Agent 3
P
I1
Agent 2
K
Infrastructure
Network
I3
Real Emergency
Domain
I – information system
K
I
P
In
P – preferences system
K
P
Agent Manager
Agent N
K – knowledge system
P
C
[Balducelli,Gadomski,1993]
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Adam M. Gadomski, 8/10/2003
IPK Bases: an example
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Adam M. Gadomski, 8/10/2003
Component Errors Modelling
IPK Cognitive
computational model
Basic Modelling
Framework
(Information, Preferences,
Knowledge) Application
Problem Specifications are:
Requested & Modified
Information
I
Motivations create proper
Preferences which activate
adequate Knowledge
P
K
Models are Knowledge
I2 = Ki I1, where Ki = P {K}
Human ERRORs: Not proper or not sufficient Information
Lack or not proper Importance Scale (Preferences, risk ass.)
Not proper or not sufficient instructions, procedures (Knowledge)
Wrong Cognitive and Organizational Factors (Motivations).
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Adam M. Gadomski, 8/10/2003
SOCIO-COGNITIVE ENGINEERING: an Intelligent Organization
General Functional Frame
Mission/Fundation-Goal
Unexpected events
Organization
Products/Actions
TOGA theory framework
Intelligent Organization  is specified by:
 (, , ODM,  )
set of roles, 
structure, 
All of them can be a cause of
decisional mechanisms, ODM , and
Vulnerability:
v(, X) .
Resources/means, , such as information network
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Adam M. Gadomski, 28/09/2003
Components: Universal Management Paradigm (UMP)
UMP includes
6 canonical
roles and their interrelations
SUPERVISOR/ COORDINATOR
tasks
information
cooperation
expertises
COOPERATING
MANAGER
MANAGER
ADVISOR
Knowledge &
Preferences
repository
information
INFORMER
tasks
with the same relative
internal structure
EXECUTOR
DOMAIN OF ACTIVITY AND MANAGER’s GOAL-DOMAIN
Fig. 1. H-Organization: A graphical illustration of Universal Management Paradigm
(UMP): the cooperating-manager environment from the subjective perspective of a preselected decision-making manager [4].
Dynamic Role Model (computational)
Definitions according TOGA
Role (competences, duties, privileges )
Competences: what he/she/it is able to do, possessed models of the
domain (knowledge)
Duties: responsibility, tasks and requested preferences
Privileges: Access to the information. It produces conceptual images of the
domain. Access to execution tools (information); org.power.
The roles are specified by their own IPK Bases Set:
Information Bases
– how situation looks, continuously updated
Preferences Bases – importance scales/relations, ethics rules
Knowledge Bases
– required models & know how
Remark: Structure depends on roles, and roles depend on IPKs
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Adam M. Gadomski, 28/09/2003
Pathologies of Organizations: Examples
Complex situation: Every human-agent is in 3 roles together :
1. Organizational role – requested/defined by the structure (fixed)
– applied, structure independent (variable)
2. Informal role
3. Personal/real role – really realized (variable)
Conflicts of Roles
Compromise, inefficient
risky decisions.
Necessity of negotiations
Dynamics of roles may create different lack of
congruence between them & conflict of interests
Conflict of Interests/Motivations
Social interest
Differ Risk-Benefits relations for
All of them influence ODM
HID
Organization interest
Personal interest
Adam Maria Gadomski, http:// erg4146.casaccia.enea.it/
High-Intelligence & Decision Research Group, 2005
Decision-Making
Knowledge Base
New Information
or task
No action/response
Decision-Making
Meta-action/Pseudo-action
Preferences Base
Action adequate to D-M’er
role and situation
Cognitive Definitions [TOGA]
Decision-making: an individual or group reasoning implied by the
request/necessity of a choice caused by received information or task, or by
delivered conclusion about possibility of risks/benefits. It is started when either
choice criteria are unknown or alternatives are unknown and finished when
choice is performed.
Action-oriented decision-making: it is a decisional process when alternatives represent
possible actions in pre-chosen physical domain.
Mental decision-making: when the final choice refers not to actions but to conceptual
objects related to a preselected domain of activity of intelligent agent.
Group decision-making: when responsibility for decision is allocated to a group of
intelligent agents and is based on shared decision-making process.
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Adam M. Gadomski, 28/09/2003
Pathologies of Decision-Making (computational models)
alternatives
reasoning path
?
critical
node
data
d-m
?
decision
decision
Types of Proper and Pathological Decisions
Main classes:
- meta-D-M ,
- pseudo D-M,
- proper D-M.
Pathologies are related to:
- response on source type ( “safety” filters );
- response on subject ( lack of competences, emotional reaction, out of Interest).
- response according domain-preferences (organizational/personal role): proper DM.
Controlability & updating of Ethics concept
If D-M autonomy increases then: Efficacy of Control decreases & Importance of Ethics and
personal motivation increases. This rule indicate importance of Motivation Management.
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Adam M. Gadomski, 28/09/2003
Pathology of Bureaucracy: two iron laws
There are two iron laws of bureaucratic behavior of the selfaggrandizement managers :
1. They tend to maximize the resources they control, usually
at the expense of their competitors within the organization.
[J. Wilson, 2005]
2. They (in different manners) tend to minimaize their own
personal risk. [G.Ridman, 2001]
The primary: such actions increase subjective security and informal
power.
The second law implies that managers tends to take only unavoidable
risks, and all decisions that seem to carry some risk to the decisionmaker will be (bucked up) as far as possible.
These laws apply equally to private- and public-sector.
Frequently the personal risk is hidden and officially “does not
exist” but it influences strongly bureaucratic decision-making,
and it is significant component of vulnerability (VoHO).
Strategies: continuous improvement
by D. Keith Denton, Creating a system for continuous improvement - improving an
organization's decision-making process. Business Horizons, Jan-Feb, 1995
•
To have continuous improvement , there has to be some factor that
binds people together.
•
There must be a common purpose, and each member must
understand his or her role.
•
If you want real, long-lasting change, then you must have a way of
focusing people on the change.
Individual motivation building is essential factor for the
organization continuous improvement and robustness.
Strategies: Human-Oriented
MANAGEMENT OF STRATEGIES:
A primary concern of every consumer of management theory is to understand
where it applies, and where it does not apply. {Paul R. Carlile, 2005]
On November 14, 2005, KMCI will hold its One-Day Workshop on
Reducing Risk by Killing Your Worst Ideas.
Most contemporary approaches to risk concentrate on assessing risk in the context of
some model being applied by the person or group assessing risk, so if that model is
false or illegitimate the risk assessment is too.
This workshop views risk assessment from this internal perspective.
It tells you how to reduce risk, particularly in business, by using both creative learning
and critical thinking.
The problem of a wrong strategy choice how to cope with vulnerability
- What is clear but How is not yet well defined.
Systemic Approach
Response STRATEGIES: TOGA
Strategy  ( A, B, C, D, E, F , . ) is a pattern for a class of
actions, it depends on attributes of ,  and R|X..
Components of a Strategy in different phases of the lifecycle of
an organization ( they have decrease vulnerability v(, X) ).
A. Learning (continuous knowledge acquisition)
B. Training (real, simulated), games
C. Motivation building (individual, group), competition
D. IDSS functions (computerized, real-time)
E. Reorganization (in crisis)
F. Bottom-up local reasoning according to clear and
accepted top-down rules (routine).
Strategies: Intelligent Infrastructures (IIN)
EC, Unit G3: "Embedded Systems"
IINs are highly autonomous systems which support services
and industrial/production systems enabling them to execute
human end-users oriented functions.
IINs are one of emergent challengers of our new century, they are
feasible for realization.
Recently, intelligent infrastructures networks or intelligent
networked infrastructures( a "multi-brain nervous system") are
becoming emergent components of embedded dependable
computer and human-computer systems.
They should lead to the building of different forms of
"collective intelligence“ (organiz-human-computer).
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Adam M. Gadomski, 23/06/2005
Abstract Intelligent Kernel for
Intelligent Infrastructures
Functional requests
We need a software module with capacity of:
- autonomy in decision making
- reasoning/inferencing in problem solving
- learning from the environment and from communication
- modification of its own goal
- modeling/identification of its world (discovery)
- knowledge and information acquisition by communication
- interaction with environment by effectors and communication.
... and TOGA includes a preliminary framework of such abstract properties.
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Adam M. Gadomski, 23/06/2005
Applications: TOGA Methodology for Intelligent Kernel Design
Based on
SPG
Approach.
From the ENEA’s Tech. Proposals of the EU Project EIDA,1996 & EMIR 2004
(Abstract Managerial Intelligence)
Infrastructure Simulation Game System
Servicies Functional Units
Interface
Intelligent Infrastructure
World
Simulator
IntelI.
Infrast.
Kernel
Interface
World Editor
Servicies
Units
Communication
“Absolute
Observer”
(designer)
Human
Supervisor
or Manager
Communication
Top view of the Infrastructure Simulation Game System
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Adam M. Gadomski, 23/06/2005
An example: Intelligent Chip for m-Learning & m-IDSS
I-Chip
Intelligence
Infrastrutture
Network
USB
PC+Web
m PC
TOGA’s-Model
Artificial Organization – mixed two webs
SUPERVISOR
tasks
Knowledge
Preferences
expertises
information
MANAGER
MANAGER
cooperation
COOPERATING
MANAGERs
ADVISORs
information
tasks
INFORMERs
EXECUTORs
(Personoids, see Web)
http://erg4146.casaccia.enea.it/wwwerg26701/perhom2.html
Domain of Activity
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Adam M. Gadomski, 23/06/2005
Computer support & substitution of human functions
% contribution to an activity/task
100
trend
50
HUMAN tasks
COMPUTER
personoids
tasks
00
Life-functions
Information Systems & DSSs or
Social-functions
(Decision-Making Sypport Systems)
Robots
Activities
Development of autonomous computer infrastructure networks
IIN: Final Remarks
“ Mission, Unit G3: "Embedded Systems" (Kostas Glinos)
To be the focal point and integrator for research in embedded
systems in Europe.
The aim is to strengthen Europe's position in pervasive, networked
and dependable embedded systems
by integrating and extending the scientific and technological base,
by promoting innovation and top-quality research and
by increasing industrial capabilities. “
----------------------------Meta-Strategic Thinking  ??
New problems dramatically need the reinforcement of the
integration of theoretical frameworks with applications.
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Adam M. Gadomski, 23/06/2005
Some References, 1
1.
A.M. Gadomski .TOGA: A methodological and Conceptual Pattern for modelling of Abstract Intelligent Agent.
In Proc. of the ‘First International Round-Table on Abstract Intelligent Agent’,25-27 Jan 1993, Enea print
(1994).
2.
A. M. Gadomski. Personoids Organizations: An Approach to Highly Autonomous Software Architectures,
“11th International Conference on Mathematical and Computer Modeling and Scientific Computing,, March
31 - April 3, 1997, Georgetown University Conference Center, Washington.
3.
A.M.Gadomski et al., Towards Intelligent Decision Support Systems for Emergency Managers: The IDA
Approach. International Journal of Risk Assessment and Management, IJRAM, 2001, Vol 2, No 3/4.
4.
A. M. Gadomski, Meta-Knowledge Engineering Server (since \997): http://erg4146.casaccia.enea.it
5.
Hannan, Michael T., and John Freeman, "Structural Inertia and Organizational Change." American
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6.
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© ENEA, 2004. A.M.Gadomski., E-mail: [email protected]
References, 2
For more information yet:
1. A.M. Gadomski , SOPHOCLES - EUREKA & MURST & ENEA: Intelligent Cognitive Systems
Engineering, Transparent-sheets, 20/09/2000, Updated 17/06/2001 ENEA , ITEA materials.
2.
A.M. Gadomski , SOPHOCLES Project – Cyber Virtual Enterprise for Complex Systems
Engineering: Cognitive Intelligent Interactions Manager for Advanced e-Design,
Transparent-sheets, 28/08/2001, ENEA. ITEA materials
3.
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A.M.Gadomski. TOGA: A Methodological and Conceptual Pattern for modeling of Abstract
Intelligent Agent.Proceedings of the "First International Round-Table on Abstract Intelligent
Agent". A.M. Gadomski (editor), 25-27 Gen., Rome, 1993, Published by ENEA, Feb.1994.
4. A.M.Gadomski, "The Nature of Intelligent Decision Support Systems". The key paper of the
Workshop on "Intelligent Decision Support Systems for Emergency Management ", Halden,
20th-21st October, 1997.
5 . A.M.Gadomski, S. Bologna, G.Di Costanzo, A.Perini, M. Schaerf. Towards Intelligent Decision
Support Systems for Emergency Managers: The IDA Approach. International Journal of Risk
Assessment and Management, 2001.
HID
Adam Maria Gadomski, http:// erg4146.casaccia.enea.it/
High-Intelligence & Decision Research Group
Thank you