CRESCO-SOC-COG Socio-Cognitive Modelling for Complex Socio-Technological Networks CRESCO Subproject III 4: CRESCO-SOC-COG Socio-Cognitive Modelling for

CRESCO Subproject III 4:

CRESCO-SOC-COGCRESCO-SOC-COG

Socio-Cognitive Modelling for Complex Socio-Cognitive Modelling for Complex Socio-Technological NetworksSocio-Technological Networks

(Modellistica delle Reti Complesse viste come Aggregati Socio-Tecnologici e Cognitivi)

Strategies, Competences and Objectives

Alessandro D'Ausilio (ECONA), Massimiliano Caramia (Tor Vergata)Adam Maria Gadomski (ENEA), Alessandro Londei (ECONA), Marta Olivetti-Belardinelli (ECONA)

5 Luglio 2006 ENEA Sede

Roma

http://web.uniroma2.it/home.php


CRESCO, Sottoprogetto III 4: CRESCO-SOC-COGCRESCO-SOC-COG

1. Objectives and General Strategy (A.M.Gadomski)

2. ECONA (A. Londei, M. Olivetti-Belardinelli, D'Ausilio ) - general information - competences - specific research interests - state of the art and the contribution3. TOR VERGATA ( M. Caramia) - general information - competences - specific research interests - state of the art and the contribution

4. ENEA (A.M.Gadomski) - competences - project planning - examples

CRESCO, Sottoprogetto III 4: CRESCO-SOC-COGCRESCO-SOC-COG

CRESCO is a large ENEA's and Italian Project focused on the research on complex systems in the different areas of science and technologies. It is based on the development of the advanced High Performance Computing infrastructure ENEA grid. CRESCO (Centro Computazionale di RicErca sui Sistemi Complessi, Computational Research Center for Complex Systems) is co-funded by the Italian Ministry of Education, University and Research (MIUR). The CRESCO project is functionally built around the HPC platform, through the creation of a number of scientific thematic laboratories, such as: Computing Science Laboratory, Computational Systems Biology Laboratory, and Complex Networks Systems Laboratory - dealing with the complexity of information technology networks and human organizations decisional dependences in large national critical services infrastructures.A major ambition of this multi-discipline project is to allow an inter-disciplinary flow of methods and ideas related to the network-based modeling and simulation of complex systems and their aggregates.

http://www.cresco.enea.it/

Sottoprogetto III 4: CRESCO-SOC-COG

GoalResearch and development of the ontology and socio-cognitive and socio-technological models which should enable:

Domain-independent computational modelling and demonstrative simulation of socio-cognitive managerial high-risk decisional processes and their interdependences. Especially in the case of the collaborative emergency management for the protection of Large Complex Critical Infrastructures (LCCI).

ModelIing and analysis of the socio-cognitive vulnerabilities of these organizations.

Objectives and General Strategy

CRESCO-SOC-COG is the activity focused on the research on the vulnerabilities of human factors in frame of networked structures of high-risk large human organizations.This new challenging cross-disciplinary objective requires a qualitatively new systemic conceptualization tool for human-technology systems, therefore, the socio-cognitive methodology involved is based on the main frameworks of the holistic TOGA meta-theory.

http://erg4146.casaccia.enea.it/wwwerg26701/Gad-toga.htm



LCCI Owners

Local administrative, govern and voluntary organizations

Decisional Politic Organs

Large Technological Networks- LCCIs

Utenti di GRT

Top-Down Vision :

Socio-Technological network for the management of Large Critical Technological Infrastructures

Identification of the Domain


Sub-project III 4: CRESCO-SOC-COG


Physical Layer

Cyber/information Layer

Organization / Management Layer

Cognitive –Sub-simbolic Layer

Socio-cognitive Simbolic Layer

Inter-organization Layer

Intra-organizational Layer

“Spetroscopy” of the conceptualization layersIdentification

Their interfaces

Sub-layers:Layers:


Multimedial Interfaces between Cyber and Organization Layer

Topological & process data

Vocal Communication

Tasks & Actions data

Cyber Layer

Organization Layer

Tasks Separation

Written Tasks & Inform.

(Ferov di Stato, L’espresso, 2006)


Conceptual Scenario-based Interface (An introduction to the CIP ontology)

Organization Layer is activated when the situation is over the (routine & emergency) competences of the operators

Operator is an autonomous informer and executor, see UMP.

Operators are human interfaces between Cyber and Organization Layers.

Organization decisions provide context and constrains for next organizational decisions.


Socio-Cognitive Domain Socio-Technological Domain

Subsymbolic D-M

Symbolic D-M

D-M autonomo

D-M collaborativo

Simulation: Piattaforme parallele software di supporto D-M : tecn. “multiagent” (MAS)

Nodi esecutivi

Nodi decisionali manageriali Unita umane supportate dal IT

zoom

Reti di Grande InfrastruttureTecnologiche dei Servizi

…

Legenda: Ri

Ri – ruolo i

R1

R2

R3

R4

R5

R6

R7

Organiz. BOrganiz. ASocio-Cognitive and Socio-technological layers

Separation of two conceptual domains of research.


Strategia Generale : ORGANIZATION COMPLEXITY

Multi-dimensional attributes space

Complex network of interactions

Continuous & Discrete Dynamics

Interactions with dynamic physical & social environment

Intelligent knowledge-based and interest-based human nodes

Autonomy of nodes

Emotional and Body contribution components

Cognitive factors: ill measurable, observable and monitored

Project requires a new innovative computational systemic methodology for the modeling.

Physics based statist. models (primitive intelligence)

New modeling paradigms (high intelligence)

Adam M. Gadomski, CAMO – ENEA – RC Casaccia

TOGA TOGA meta-theory meta-theoryTOGA is a formal goal-oriented knowledge ordering meta-theory, its objective is to enable design of complex systems & their computer simulation. It has three basic components:- Theory of Abstract Objects (TAO) is a first level and a basic domain independent conceptualization system and a consensus building platform;- Knowledge Conceptualization System (KNOCS), It includes TOGA’s ontology, i.e. axiomatic assumptions and basic conceptualization frameworks for the definition and decompositions of the real-world problem into an intelligent agent (IA) and domains of IA goal-oriented activities, i.e. the triple: (Intelligent Entity, Environment, Interactions)- Methodological Rules System (MRUS) for the specification (if not existing yet) or identification (if existing) of complex systems and problems; it indicates how TAO and KNOCS have to be used during the conceptual identification, specification and solution of real word problems. The KNOCS meta-frameworks includes four modeling paradigms:1. Universal Reasoning Frame Paradigm (URP), it is based on the IPK (Information, Preferences, Knowledge) architecture.2. Universal Management Paradigm (UMP), it includes management functional definition and a conceptualization of the context of the managerial role. 3. SPG Universal Domain Paradigm (UDP), it is a framework of the conceptualization of the relation between an organization and its foundation-goal in terms of: systems, processes, functions and design-goals. 4. WAG Universal Activity Paradigm (UAP) , conceptualization of the relation between a problem world and a goal of intervention of intelligent agent .

Modelling Tool:

TTop-down op-down OObject-based bject-based GGoal-oriented oal-oriented AApproachpproach

[CNIP’06 Conf.]


Cognitive complex networks modeling

First project cog-engineering hypothesis: Abstract Cognitive Architecture of Decision-Maker is based on 4 types of reasoning processes reciprocally interacting and developed on the recursive, incremental and multi-layered IPK based network (with a fractal property ).

Not conscious reasoning Conscious reasoning

Subsymbolic Neural Networks

Genetic Algorithms

Images-Based Associations

Symbolic Associative networks Procedural-relational networks

Associative Networks Cause-consequence networks Model based network

IP K

Domains of possible research

TOGA


Organizational complex networks modeling

Second project engineering hypothesis: Universal Management Paradigm (UMP) defines the manager environment from the subjective perspective of a pre-selected decision-making manager [4] which can be projected on real role-networks of human organizations.

DOMAIN OF ACTIVITY AND MANAGER’s GOAL-DOMAIN

EXECUTOR

information tasks

ADVISOR

expertises COOPERATINGMANAGER

cooperation

SUPERVISOR/ COORDINATOR

tasks information

Knowledge & Preferences repository

INFORMER

MANAGERMANAGER

with the same relative internal structure

Recursive incremental model


Identification of Vulnerability The presented modeling frames enable identification of different types of organizational vulnerability: on individual levels, for group d-m, and cooperative intra-organizational types.

Using IPK

I

KPgoal

Domain of Activity

n

We may distinguish:

- Not sufficient information

- Not proper preferences

-Not adequate competences (knowledge).

- Improper communication

Using UMP

MANAGER

INFORMER EXECUTOR

information tasks

ADVISOR

expertisesCOOPERATING

MANAGER

cooperation

tasks information

Knowledge Preferences

The same structure

SUPERVISOR

Domain of management (Domain of activities)

Vulnerabilities

Identification of Vulnerability


Human organization is a system/network with explicitly established reciprocal dependencies between people, which, according to their competences, collaborate for achieving common objectives or realize predefined missions.The concepts: vulnerability, crisis and emergency are well visible in this generic h-orgnization life-cycle picture where they can be, in different manner, allocated to the organization phases.

Foundation

Self-organization

Proper Activity

Re-organization

Proper Activity

Pathological organization

Healthy organization

Organization in recovery

We distinguish three necessary critical efficacy levels:-Survive efficacy, Ef0. - Emergency critical efficacy, Ef1 - Routine critical efficacy, Ef2 (enables a bureaucratic functioning)

Time

Ef0

Ef2

Ef1

Crisis

Efficacy Proper activity phase Re-organization

Vulnerability

Vulnerability

Self-org phase

H-Organization Life-cycle

Qualitative illustration Gadomski,2005,

Identification of Vulnerability


ENEA

ECONA

DII - TOR VERGATA

Partners

Cognitive Layer

Socio-technological Layer

Integration Methodology

Socio-cognitive Layer

Top Tasks Allocation


ECONA (1) IInteruniversity Center for Research on Cognitive Processing in Natural and Artificial Systems

Informazione Generale

ECONA is an inter-university and cross-disciplinary center providing teaching staff and researchers in which participate 12 Italian universities.

It is focused on the studies of cognitive processes and collaborates on research projects and their practical applications.

ECONA collaborates with research projects (including projects financed by MURST, CNR, the EEC and the European Science Foundation) and covers the following areas:

Psychology of cognitive processes

Mental process models

Neural networks and genetic programming

Non-linear dynamic behaviour

Natural language processing

Logic, languages and methods in programming

Psychophysiology and neuropsychology

Pedagogic communication

Education with intelligent processor support

Probabilistic approaches to situations of uncertainty

Learning processes

Members of ECONA represent inter-dysciplinary competences: psychology, philosophy, physics, computer science, mathematics, engineering, medicine and organization sciences.


PRESA DI DECISIONE

Tempo ridotto

Complessità della rappresentazione problemica

Stress e Responsabilità

ERRORE

Ma se non esiste una risposta sempre “Corretta”?

ECONA (2): Cognitive Perspective on Decision Making


IMPLEMENTATION

• Situazioni artificiali e semplificate

> controllo

< aderenza realtà

• Situazioni reali

< controllo

> aderenza realtà

ECONA (3)


SCOPI

Individuare classi di risposte divise per tipologie di personalità DECISONALI

• Strategie Analitiche VS. Globali (Complessità)

• Fast VS Slow responder (Tempo)

• Strategie di Coping (Stress e Responsabilità)

ECONA (4)


QUANDO - COME - PERCHE’si commettono errori

• Previsione errori “macchina” uomo

• Supporto decisionale situazioni critiche

• Controllo in real-time

• Simulazione effetti della decisione

ECONA (5)


ECONA (6)

from from TOGASOM

ClusterAnalysis

Behavioral prototypes

Architectureselection

Plasticityselection

Genetic algorithmsGenetic algorithms

geneticpopulation

fitness crossing-over mutationsnext

population


ECONA (7) Automatic Model Generation Possibility


ECONA (8) Automatic Model Generation Possibility

• selection of artificial agents whose behavior is compatible with natural observation

• decisional choosing is driven by environmental pressure

• determination of suitable recurrent neural architectures for best results by means of genetic algorithms

• analysis of neural spatial and functional distribution for detecting functional areas involved in decisional task

• extension to socio-cognitive networks of evolved agents

Decisional agentsgenetic modelization

Fastness Efficency

Behavioral support topsychological and artificial

observations


ECONA (9) From Cognitive Models to Socio-Cognitive Networks


Dipartimento di Ingegneria dell’Impresa Università di Roma “Tor Vergata”

Competenze e Aree principali di ricerca

• Modellazione dei processi gestionali• Ottimizzazione e simulazione di sistemi complessi• Ottimizzazione su reti• Metodi e modelli per il supporto alle decisioni• Logistica e Produzione



• Interesi particolari riferiti ai goal del progetto– Ottimizzazione in ambiente on-line

– Modellazione di sistemi di produzione-servizio tramite agenti autonomi

– Modellazione di problemi decisionali in presenza di più decisori con presenza o assenza di cooperazione e/o negoziazione

– Simulazione di sistemi organizzati in scenari tattici e/o operativi

• Risorse: professori, post-doc, laureandi

DII - TOR VERGATA



• Esperienza in progetti su argomenti correlati: Progetto Strategico CNR su “La gestione delle emergenze nelle organizzazioni complesse”; 9 unità perative coordinate da Tor Vergata; anni 2000-2002.

• Conoscenza e uso dei diversi Strumenti di Progettazione Concettuale.

DII - TOR VERGATA

ESPERIENZA PREGRESSA



Case Based Analysis e Modellazione

• Analisi di casi reali scelti e la modellazione sintetica (organizational networks)

• Simulazione (tipo demo) di casi di studio relativi a situazioni di crisi rilevanti in cooperazione tra organizzazioni complesse, al fine di individuare le cause di vulnerabilità e definire le azioni correttive al loro interno.

• Attività successive proposte: prototipizzazione del tool di simulazione con possibilità da parte dell’utente di effettuare opportuni tuning del sistema per il suo controllo.

DII - TOR VERGATA:

Possible Contributions



• competenze

• pianificazione• esempi

ENEA

Keywords:

Systemistic Modelling and Top-down approach

Socio-Cognitive Engineering

Meta-Knowledge Engineering & Management

Decision-Making

Intelligence

Ontology Building Methodology

Abstract Intelligent Agents

Organizational Intelligence

Organizational Vulnerability

Simulation Modelling


1. Data & Modeling year

2. Designing & Implementing year

3. Improvement & Validation year

1. Data & Modeling year

- Data acquisition

- Proper modeling

- Model validation

PLANNING

ENEA

2. Designing & Implementing year

- Parallelization

- Implementation

- Validation

3. Improvement & Validation year

- Improvements

- Test cases

- Validation


ENEA

Data Acquisition: EXPERIMENTS

For Cognive Decision-Making

For Organizational Decision-Making

Identification of Socio-Cognitive Vulnerabilities

EXAMPLE: an identification of the Incidents and its main observables(Key factors)


Source: L’espresso, 6 Luglio 2006

• EXAMPLE: an identification of the key factors of human errors:

…IPK 65%


METHODOLOGICAL Framework

We have:

Experimental observables and Theory observables.

Experimental observables

Experimental factors (key factors)

Domain Model

Theory Ontology

Preference

Informazioni

ConoscenzaTheory observables

MODEL SPECIALIZATION

SIMULATIOR DESIGNSource: L’espresso, 6 Luglio 2006


ENEA

Analyzed Test Cases

1. Blackout Italy/Suisse , 28 september 2003

2. Chernobyl disaster

3. Katrina hurricane

4. Airport Linate accident

5. Tsunami: international scale catastrophe –Indian

Ocean

P,Sargeni, L’ergonomia cognitiva nella vulnerabilità delle organizzazioni: la prospettiva socio-cognitiva di TOGA. Facolta Science di Comunicazione, Univ. La Sapienza.,ENEA, 2006.


Preliminary Test Cases Results

Ruoli

Casi

Supervisor

Manager CooperatingManager

Advisor Informer Executor

ItalianBlackout

Chernobyl

Linate

Katrina

Tsunami

Identification of vulnerability on the IPK level and according to the UMP roles.

Legenda:

Il problema si è verificato sul livello delle Informazioni Nessun valore dominante

Il problema si è verificato sul livello delle Preferenze

Il problema si è verificato sul livello delle Conoscenze

Possible results hypotheses

How, is possible to improve organiz, robustness/(decrease vulnerability)?

Hypotheses:

- by modifications of organization architecture

- by modifications of roles (comp, respons, power)

-- computer support, automatic distribution IPK in organization

according to the org. roles of nodes.

-- Providing these structures more transparent

-- Modification and adaptation of distributed DM procedures


BIBLIOGRAFIA• Rassmussen, Jens 1988. A cognitive engineering approach to the modeling of decision making and its

organization in process control, emergency management, CAD/CAM, office systems, and library systems. Advances in Man-machine Systems Research 4: 165--243

• A.M. Gadomski (2004). Humam Organization Crisis: Identification, Response & Recovery, http://192.107.74.146/gad-crisis.htm

• A.M. Gadomski, 2002, Systemic Approach for the Sophocles Global Specification,. http://hid.casaccia.enea.it/RepSoph-v10.pdf http://erg4146.casaccia.enea.it/wwwerg26701/Gad-toga.htm• R.J. Sternberg, Triarchic Theory of Intelligence,.• Tesi di laurea: D. Ricciardi Analisi della vulnerabilità del business aziendale e del Knowledge Management

secondo la prospettiva della teoria TOGA, 2005,, Univ. Tor Vergata. P,Sargeni, L’ergonomia cognitiva nella vulnerabilità delle organizzazioni: la prospettiva socio-

cognitiva di TOGA. Facolta Science di Comunicazione, Univ. La Sapienza., 2006.• Materiali del Progetto IRRIIS, 2006• L’ESPRESSO., N.26, 6 Lug. 2006• Siti web:

- Google search: CIIP, CIP, cognitive: http://www.google.it/search?hl=it&q=CIIP%2C+CIP%2C+cognitive&btnG=Cerca&meta=

– the European co-ordination project on Critical Information Infrastructure Research Co-ordination. http://www.ci2rco.org/index.asp

– http://w3.uniroma1.it/security/Eventi/Sciascia.pdf– www.cnipa.gov.it/site/_files/pres-MEROLAroma.PPT– Relazione sulle attività svolte dall'ENEA -CAMO, 2005 :

http://hid.casaccia.enea.it/activity2005.htm…


http://192.107.74.146/gad-crisis.htm

http://hid.casaccia.enea.it/RepSoph-v10.pdf


http://www.google.it/search?hl=it&q=CIIP%2C+CIP%2C+cognitive&btnG=Cerca&meta






http://www.ci2rco.org/index.asp

http://www.cnipa.gov.it/site/_files/pres-MEROLAroma.PPT

http://hid.casaccia.enea.it/activity2005.htm


Grazie

Documents

CRESCO-SOC-COG Socio-Cognitive Modelling for Complex Socio-Technological Networks CRESCO Subproject III 4: CRESCO-SOC-COG Socio-Cognitive Modelling for