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

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Presentation on theme: "CRESCO-SOC-COG Socio-Cognitive Modelling for Complex Socio-Technological Networks CRESCO Subproject III 4: CRESCO-SOC-COG Socio-Cognitive Modelling for."— Presentation transcript:

1 CRESCO-SOC-COG Socio-Cognitive Modelling for Complex Socio-Technological Networks CRESCO Subproject III 4: CRESCO-SOC-COG Socio-Cognitive Modelling for Complex Socio-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 July 2006 ENEA Sede Roma English Extended Version (Ed. A.M.Gadomski) 20 November 2006

2 CRESCO-SOC-COG CRESCO, Sottoprogetto III 4: CRESCO-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 contribution 3. 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

3 CRESCO-SOC-COG CRESCO, Sottoprogetto III 4: CRESCO-SOC-COG CRESCO 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.

4 Sottoprogetto III 4: CRESCO-SOC-COG Goal Research 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. socio-cognitive TOGA meta-theory

5 Sottoprogetto III 4: CRESCO-SOC-COG 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 Objectives and General Strategy

6 Sub-project III 4: CRESCO-SOC-COG Objectives and General Strategy 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 layers Identification Their interfaces Sub-layers:Layers:

7 Sottoprogetto III 4: CRESCO-SOC-COG 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, Lespresso, 2006)

8 Sottoprogetto III 4: CRESCO-SOC-COG Conceptual Scenario-based Interface: An introduction to the CIP (Critical Infrastructure Protection) 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. Adam M. Gadomski, CAMO – ENEA – RC Casaccia

9 Sottoprogetto III 4: CRESCO-SOC-COG Socio-Cognitive DomainSocio-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 Infrastrutture Tecnologiche dei Servizi … Legenda: Ri Ri – ruolo i R1R1 R2R2 R3R3 R4R4 R5R5 R6R6 R7R7 Organiz. BOrganiz. A Socio-Cognitive and Socio-technolo- gical layers Separation of two conceptual domains of research.

10 Sottoprogetto III 4: CRESCO-SOC-COG 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

11 TOGA meta-theory TOGA meta-theory TOGA 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 TOGAs 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: T op-down O bject-based G oal-oriented A pproach [CNIP06 Conf.]

12 Sottoprogetto III 4: CRESCO-SOC-COG 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 reasoningConscious reasoning Subsymbolic Neural Networks Genetic Algorithms Images-Based Associations Symbolic Associative networks Procedural-relational networks Associative Networks Cause-consequence networks Model based network I PK Domains of possible research TOGA

13 Sottoprogetto III 4: CRESCO-SOC-COG 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 MANAGERs GOAL-DOMAIN EXECUTOR informationtasks ADVISOR expertises COOPERATING MANAGER cooperation SUPERVISOR/ COORDINATOR tasks information Knowledge & Preferences repository INFORMER MANAGER with the same relative internal structure Recursive incremental model

14 Sottoprogetto III 4: CRESCO-SOC-COG 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 K P goal Domain of Activity n We may distinguish: - Not sufficient information - Not proper preferences -Not adequate competences (knowledge). - Improper communication Using UMP MANAGER INFORMER EXECUTOR informationtasks ADVISOR expertises COOPERATING MANAGER cooperation tasksinformation Knowledge Preferences The same structure SUPERVISOR Domain of management (Domain of activities) Vulnerabilities Identification of Vulnerability

15 Sottoprogetto III 4: CRESCO-SOC-COG 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, Ef 0. - Emergency critical efficacy, Ef 1 - Routine critical efficacy, Ef 2 (enables a bureaucratic functioning ) Time Ef 0 Ef 2 Ef 1 Crisis Efficacy Proper activity phase Re-organization Vulnerability Self-org phase H-Organization Life-cycle Qualitative illustration Gadomski,2005, Identification of Vulnerability

16 Sottoprogetto III 4: CRESCO-SOC-COG ENEA ECONA DII - TOR VERGATA Partners Cognitive Layer Socio-technological Layer Integration Methodology Socio-cognitive Layer Top Tasks Allocation

17 Sottoprogetto III 4: CRESCO-SOC-COG I ECONA (1) Interuniversity 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 behavior 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-disciplinary competences: psychology, philosophy, physics, computer science, mathematics, engineering, medicine and organization sciences.

18 Sottoprogetto III 4: CRESCO-SOC-COG Decision-Making 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

19 Sottoprogetto III 4: CRESCO-SOC-COG IMPLEMENTATION Situazioni artificiali e semplificate > controllo < aderenza realtà Situazioni reali < controllo > aderenza realtà ECONA (3)

20 Sottoprogetto III 4: CRESCO-SOC-COG SCOPI Individuare classi di risposte divise per tipologie di personalità DECISONALI Strategie Analitiche VS. Globali (Complexity) Fast VS Slow responder (Time) Strategie di Coping (Stress, responsibility) ECONA (4)

21 Sottoprogetto III 4: CRESCO-SOC-COG QUANDO - COME - PERCHE si commettono errori Previsione errori macchina uomo Supporto decisionale situazioni critiche Controllo in real-time Simulazione effetti della decisione ECONA (5)

22 Sottoprogetto III 4: CRESCO-SOC-COG ECONA (6) from from TOGA SOM Cluster Analysis Behavioral prototypes

23 Architecture selection Plasticity selection Genetic algorithms genetic population fitnesscrossing-overmutations next population Sottoprogetto III 4: CRESCO-SOC-COG ECONA (7) Automatic Model Generation Possibility

24 Sottoprogetto III 4: CRESCO-SOC-COG ECONA (8) Automatic Model Generation Possibility selection of the properties of artificial agents whose behavior is compatible with natural observation decisional-making is under/(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

25 Decisional agents genetic modelization FastnessEfficency Behavioral support to psychological and artificial observations Sottoprogetto III 4: CRESCO-SOC-COG ECONA (9) From Cognitive Models to Socio-Cognitive Networks

26 Sottoprogetto III 4: CRESCO-SOC-COG Dipartimento di Ingegneria dellImpresa 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

27 Sottoprogetto III 4: CRESCO-SOC-COG 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

28 Sottoprogetto III 4: CRESCO-SOC-COG 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 Conoscenza e uso dei diversi Strumenti di Progettazione Concettuale. DII - TOR VERGATA ESPERIENZA PREGRESSA

29 Sottoprogetto III 4: CRESCO-SOC-COG 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 dellutente di effettuare opportuni tuning del sistema per il suo controllo. DII - TOR VERGATA: Possible Contributions

30 Sottoprogetto III 4: CRESCO-SOC-COG competences planning esamples 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

31 Sottoprogetto III 4: CRESCO-SOC-COG 1. Data & Modeling phase 2. Designing & Implementing phase 3. Improvement & Validation phase 1. Data & Modeling phase - Data acquisition - Proper modeling - Model validation PLANNING ENEA 2. Designing & Implementing phase - Parallelization - Implementation - Validation 3. Improvement & Validation phase - Improvements - Test cases - Validation

32 Sub-project III 4: CRESCO-SOC-COG ENEA Data Acquisition: EXPERIMENTS For Cognive Decision- Making For Organizational Decision- Making Identification of Socio-Cognitive Vulnerabilities

33 EXAMPLE: an identification of the Incidents and their main observables (Key factors) Sottoprogetto III 4: CRESCO-SOC-COG Source: Lespresso, 6 Luglio 2006

34 EXAMPLE: an identification of the key factors of human errors: … IPK 65% Sottoprogetto III 4: CRESCO-SOC-COG METHODOLOGICAL Framework We have: Experimental observables and Theory observables. Experimental observables Experimental factors (key factors) Domain Model Theory Ontology Preference Informazioni Conoscenza Theory observables MODEL SPECIALIZATION SIMULATIOR DESIGN Source: Lespresso, 6 Luglio 2006

35 Sub-project III 4: CRESCO-SOC-COG ENEA Analyzed Test Cases 1. Blackout Italy/Suisse, 28 september Chernobyl disaster 3. Katrina hurricane 4. Airport Linate accident 5. Tsunami: international scale catastrophe –Indian Ocean P,Sargeni, Lergonomia cognitiva nella vulnerabilità delle organizzazioni: la prospettiva socio-cognitiva di TOGA. Facolta Science di Comunicazione, Univ. La Sapienza.,ENEA, 2006.

36 Sub-project III 4: CRESCO-SOC-COG Preliminary Test Cases Results Ruoli Casi Supervisor ManagerCooperating Manager AdvisorInformerExecutor It alian Blackout 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 [P,Sargeni ]

37 Possible results hypotheses How, is possible to improve organiz, robustness/(decrease vulnerability)? Hypotheses: - by modifications of organization architecture - by modifications of roles (competences, responsibility, power) - by increasing of the consciousness on the emotional components of DM -- computer support, automatic distribution IPK in organization according to the organizational roles of nodes. -- Providing these structures more transparent -- Modification and adaptation of distributed DM procedures Sub-project III 4: CRESCO-SOC-COG

38 References Jens Rassmussen, 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: A.M. Gadomski (2004). Humam Organization Crisis: Identification, Response & Recovery, A.M. Gadomski, 2002, Systemic Approach for the Sophocles Global Specification,. R.J. Sternberg, Triarchic Theory of Intelligence,. Student theses: D. Ricciardi, Analisi della vulnerabilità del business aziendale e del Knowledge Management secondo la prospettiva della teoria TOGA, 2005,, Univ. Tor Vergata. P,Sargeni, Lergonomia cognitiva nella vulnerabilità delle organizzazioni: la prospettiva socio- cognitiva di TOGA. Facolta Science di Comunicazione, Univ. La Sapienza, Selected ENEAs materials of the IRRIIS Project, 2006 LESPRESSO., N.26, 6 Lug Web: - Google search: CIIP, CIP, cognitive: –the European co-ordination project on Critical Information Infrastructure Research Co- ordination. – – –Activity progress report: Relazione sulle attività svolte dall'ENEA -CAMO, 2005 : … Sub-project III 4: CRESCO-SOC-COG

39 References ECONA DII-U.Tor Vergata

40 Sub-project III 4: CRESCO-SOC-COG Grazie

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