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Semionics: A Proposal for the Semiotic Modeling of Organizations

This proposal explores the use of semiotics in modeling organizations, focusing on the sign process and the synthesis of natural and artificial systems. It introduces the concept of semionic networks and their application in discrete event dynamics and concurrent processes. The proposal suggests the use of colored Petri nets and semionic agents to model complex networks of semiosic processes occurring in real time. It also discusses the evaluation, attribution, assimilation, and generation phases involved in the semiotic modeling of organizations.

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Semionics: A Proposal for the Semiotic Modeling of Organizations

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  1. Semionics: A Proposal for the Semiotic Modeling of Organizations Ricardo Ribeiro Gudwin DCA-FEEC-UNICAMP

  2. Semiotics and Semionics • Semiotics • Science which studies the phenomena of signification, meaning and communication in natural and artificial systems • Main artifact: the sign • Tries to model any kind of phenomena as being a sign process • Natural Systems • Semiotic Analysis • Artificial Systems • Semiotic Analysis • Semiotic Synthesis • Semionics • One particular proposal for semiotic synthesis

  3. Diadic Model of the Sign

  4. Triadic Model of the Sign

  5. Semiotics x Semionics Sign Interpretant Object

  6. Semiotics x Semionics Interpreter (Semionic Agent) Sign (Signlet) Interpretant (Signlet) R1 (e.g. symbolic) R2 (e.g. iconic) Object

  7. Exosemiotics and Endosemiotics Exosemiotic View Interpreter (Semionic Agent) Sign (Signlet) Interpretant (Signlet) Internally Endosemiotic View

  8. Endosemiotic Process Modeling • From the point of view of Semiotic Synthesis • Endosemiotic understanding of the interpreter is very much important ! • Exosemiosic Process • Composed of many intrincate endosemiosic processes • Complex network of semiosic processes occurring in parallel and in real time • If we want to model (and build) such an endosemiotic system • We need a modeling artifact able to support these requisites • Discrete event dynamics • Concurrent processes • Petri Nets

  9. Endosemiotic Process Models • Petri Nets are not enough ! • Tokens are unstructured and transitions have no processing capabilities • Coloured Petri Nets (Object-based Petri Nets) • Tokens are structured • Transitions have (some) processing capabilities • Coloured Petri Nets (Object-based PN) are not enough ! • Do not differentiate among tokens • Tokens which are interpreters • Tokens which are signs • Solution • Create a new extension of a Petri Net • Semionic Networks

  10. Semionic Network: Action Semionic Agent (micro-interpreter) Signlet (sign) Signlet (interpretant)

  11. Semionic Network: Decision ?? ??

  12. Semionic Agent • Two Tasks • Decision • Choose which sign it is going to interpret • Decide what is going to happen to it (preserved or not) • Action • Turn it into an interpretant • Decision • Evaluation Phase • Attribution Phase • Action • Assimilation Phase • Generation Phase

  13. Signlets • Split into compartments • Organized into classes, according to compartment types Signlet Data or Function

  14. Semionic Agents are Signlets • Compartments • Sensors • Effectors • Internal states • Mediated Transformation Functions • Evaluation • Transformation I1 F1 F2 S1 S2 E1 I2 I3 E2 perform perform eval eval

  15. Evaluation Phase • Evaluation Phase • Starts when a given semionic agent sets up to which signlets it is going to interact to • The semionic agent must evaluate each available signlet and decide what it is going to happen to it after the interaction • For each transformation function available at the semionic agent • A set of interacting signlets of the right kind is determined • The semionic agent tests all possible combinations of available signlets which can be compatible to the inputs of its transformation functions

  16. Evaluation Phase • Enabling Scope • Each possible combination which is compatible to a given transformation function • List of signlets potentially available for interaction • Evaluated by means of an evaluation function • Should determinate if signlets are to be modified, returned to their original places or destroyed • The Phase ends when • The semionic agent evaluates all available enabling scopes and attributes to each one an interest value and a pretended access mode • The pretended access mode describes the semionic agent’s intentions to each input signlet. It should inform if the semionic agent pretends the sharing of the signlet with other semionic agents and if it intends to destroy the signlet after the interaction

  17. Evaluation Phase Signlets ?? DESTROY ? ?? Semionic Agent ??$$ ??$$ F1 ?? ?? ??$$ F2 ?? ?? ?? Fn ?? SHARE ? WHICH F ?

  18. Attribution Phase • Attribution Phase • A central supervisor algorithm gets the intentions of each active semionic agent and attributes to each of them an enabling scope • This attribution should avoid any kind of conflict with the wishes of other semionic agents • Many different algorithms can be used in this phase • For test purposes, our group developped an algorithm (Guerrero et. al. 1999), which we called BMSA (Best Matching Search Algorithm), • Attributes a signlet to the the semionic agent that best rated it, respecting the pretended access modes of each semionic agent

  19. Assimilation Phase • Depending on the Access Mode • Read: Get a reference to a Signlet, so it can have access to its internal content • In this case, the semionic agent is supposed not to change the internals of the signlet • Get: Fully assimilate the input signlets, becoming the owner of it • In this case, the semionic agent is allowed to further process it • After assimilating the necessary information • Leave the signlet in its original place • Destroy it permanently (consume it) • Take it from its original place in order to process it

  20. Generation Phase • Generation Phase • Get available information • The information collected from input signlets is used to generate a new signlet or to modify an assimilated signlet • Process it • Any kind of transformation function can be applied in order to generate new information • Send it to outputs • Signlets are sent to their corresponding outputs

  21. Special Cases • Sources • In this case, the internal functions don’t have inputs, only outputs • The result is that signlets are constantly being generated and being inserted into the semionic network • Sinks • In this case, the internal functions don’t have outputs, just inputs • These semionic agents are used to take signlets from the network and destroy them • Sources and Sinks can be used to link a semionic network to external systems

  22. SNToolkit – The Semionic Networks Toolkit

  23. SNToolkit – The Semionic Networks Toolkit

  24. Organizational Processes • Organization • Network of Resource Processing Devices performing a purposeful role • Resources • Abstract concept that can be applied to many different domains of knowledge • May have an associated “value” or “cost”, which can be used on the models being developped • Kinds of Resources • Passive Resources (materials or information) • Active Resources (processual resources)

  25. Organizational Processes • Passive Resources • Information • Texts, documents, diagrams, data, sheets, tables, etc… • Materials • Objects, parts, products, raw-materials, money, etc.. • Active Resources (Processual Resources) • Execute activities of resource processing • Mechanic (Without Decision-making) • Intelligent (With Decision-making) • Examples • Machines, Human Resources (Workers), etc…

  26. Organizational Processes and Semionic Networks • Organizational Processes • Can be described in terms of sign processes • Organizational Semiotics • Resources • Can be modeled in terms of signlets and semionic agents • Passive Resources: signlets • Active Resources: semionic agents • Networks of Resource Processing • Can be modeled in terms of Semionic Networks • Both Intelligent and Mechanical Active Resources • Can be modeled in terms of semionic agents

  27. Organizational Processes and Semionic Networks • The Interesting Case: Intelligent Active Resources • Mechanical Processes can be easily modeled by standard Petri Nets • From Peircean Semiotics • Notions of Abduction, Deduction and Induction • Abduction • Generation of newer knowledge structures • Deduction • Extraction of explicit knowledge structures from implicit knowledge structures • Induction • Evaluation of a given knowledge structure in terms of the system purposes

  28. Organizational Processes and Semionic Networks • Semionic Agents • Are able to perform decision-based actions • Coordination Between Evaluation and Transformation Functions • Allows a semionic agent to perform the three main semiosic steps: abduction, deduction and induction • The coordinated work of many semionic agents • May allow the representation of full semiotic processes • In this sense • We say that the actions performed by semionic agents are mediated actions – the transformation function is mediated by the evaluation function

  29. Example: Pizza Delivery Organization

  30. What Can we Possibly Do ? • Modeling and Simulation of Organizations • Multiples levels of abstraction • Focusing on the resources processed and on the deliverables created • Test and Simulate Multiple Configurations • Simulated re-engineering of organizations • Formal Model in order to better understand the dynamics of an organization • Build Information Systems • Better suited to the organizational structure, and which better represent the control demands of organizations

  31. Conclusions • Semionic Networks • Are a potentially interesting tool for the semiotic modeling of organizations • There is still a lot to do ! • Better integration of semionic networks to other approaches used in the study of organizations and workflows • Workflow Management Coalition Standards • Enterprise Distributed Object Computing – OMG-EDOC • Other models of business processes • Study case of complex real organizations • Only demos have been generated until now • Real study-cases may suggest new features to be included on the tool • Better understanding of the semiotic contributions to this kind of modeling

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