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

Semiotics and Semionics zSemiotics yScience which studies the phenomena of signification, meaning and communication in natural and artificial systems yMain artifact: the sign yTries to model any kind of phenomena as being a sign process zNatural Systems ySemiotic Analysis zArtificial Systems ySemiotic Analysis ySemiotic Synthesis zSemionics yOne particular proposal for semiotic synthesis

Diadic Model of the Sign

Triadic Model of the Sign

Semiotics x Semionics Sign Interpretant Object

Semiotics x Semionics Interpreter (Semionic Agent) Sign (Signlet) Interpretant (Signlet) Object R 1 (e.g. symbolic) R 2 (e.g. iconic)

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

Endosemiotic Process Modeling zFrom the point of view of Semiotic Synthesis yEndosemiotic understanding of the interpreter is very much important ! zExosemiosic Process yComposed of many intrincate endosemiosic processes yComplex network of semiosic processes occurring in parallel and in real time zIf we want to model (and build) such an endosemiotic system yWe need a modeling artifact able to support these requisites xDiscrete event dynamics xConcurrent processes yPetri Nets

Endosemiotic Process Models zPetri Nets are not enough ! yTokens are unstructured and transitions have no processing capabilities zColoured Petri Nets (Object-based Petri Nets) yTokens are structured yTransitions have (some) processing capabilities zColoured Petri Nets (Object-based PN) are not enough ! yDo not differentiate among tokens xTokens which are interpreters xTokens which are signs zSolution yCreate a new extension of a Petri Net ySemionic Networks

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

Semionic Network: Decision ??

zTwo Tasks yDecision xChoose which sign it is going to interpret xDecide what is going to happen to it (preserved or not) yAction xTurn it into an interpretant zDecision yEvaluation Phase yAttribution Phase zAction yAssimilation Phase yGeneration Phase Semionic Agent

Signlets zSplit into compartments yOrganized into classes, according to compartment types Data or Function Signlet

Semionic Agents are Signlets zCompartments ySensors yEffectors yInternal states yMediated Transformation Functions xEvaluation xTransformation S1E1 I1 eval S2 E2 I2I3 perform F1 eval perform F2

zEvaluation Phase yStarts when a given semionic agent sets up to which signlets it is going to interact to yThe semionic agent must evaluate each available signlet and decide what it is going to happen to it after the interaction zFor each transformation function available at the semionic agent yA set of interacting signlets of the right kind is determined yThe semionic agent tests all possible combinations of available signlets which can be compatible to the inputs of its transformation functions Evaluation Phase

zEnabling Scope yEach possible combination which is compatible to a given transformation function yList of signlets potentially available for interaction yEvaluated by means of an evaluation function yShould determinate if signlets are to be modified, returned to their original places or destroyed zThe Phase ends when yThe semionic agent evaluates all available enabling scopes and attributes to each one an interest value and a pretended access mode yThe 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 Evaluation Phase

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

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

zDepending on the Access Mode yRead: Get a reference to a Signlet, so it can have access to its internal content xIn this case, the semionic agent is supposed not to change the internals of the signlet yGet: Fully assimilate the input signlets, becoming the owner of it xIn this case, the semionic agent is allowed to further process it zAfter assimilating the necessary information yLeave the signlet in its original place yDestroy it permanently (consume it) yTake it from its original place in order to process it Assimilation Phase

zGeneration Phase yGet available information xThe information collected from input signlets is used to generate a new signlet or to modify an assimilated signlet yProcess it xAny kind of transformation function can be applied in order to generate new information ySend it to outputs xSignlets are sent to their corresponding outputs Generation Phase

Special Cases zSources yIn this case, the internal functions don’t have inputs, only outputs yThe result is that signlets are constantly being generated and being inserted into the semionic network zSinks yIn this case, the internal functions don’t have outputs, just inputs yThese semionic agents are used to take signlets from the network and destroy them zSources and Sinks can be used to link a semionic network to external systems

SNToolkit – The Semionic Networks Toolkit

Organizational Processes zOrganization yNetwork of Resource Processing Devices performing a purposeful role zResources yAbstract concept that can be applied to many different domains of knowledge yMay have an associated “value” or “cost”, which can be used on the models being developped zKinds of Resources yPassive Resources (materials or information) yActive Resources (processual resources)

Organizational Processes zPassive Resources yInformation xTexts, documents, diagrams, data, sheets, tables, etc… yMaterials xObjects, parts, products, raw-materials, money, etc.. zActive Resources (Processual Resources) yExecute activities of resource processing xMechanic (Without Decision-making) xIntelligent (With Decision-making) yExamples xMachines, Human Resources (Workers), etc…

Organizational Processes and Semionic Networks zOrganizational Processes yCan be described in terms of sign processes yOrganizational Semiotics zResources yCan be modeled in terms of signlets and semionic agents xPassive Resources: signlets xActive Resources: semionic agents zNetworks of Resource Processing yCan be modeled in terms of Semionic Networks zBoth Intelligent and Mechanical Active Resources yCan be modeled in terms of semionic agents

Organizational Processes and Semionic Networks zThe Interesting Case: Intelligent Active Resources yMechanical Processes can be easily modeled by standard Petri Nets zFrom Peircean Semiotics yNotions of Abduction, Deduction and Induction zAbduction yGeneration of newer knowledge structures zDeduction yExtraction of explicit knowledge structures from implicit knowledge structures zInduction yEvaluation of a given knowledge structure in terms of the system purposes

Organizational Processes and Semionic Networks zSemionic Agents yAre able to perform decision-based actions zCoordination Between Evaluation and Transformation Functions yAllows a semionic agent to perform the three main semiosic steps: abduction, deduction and induction zThe coordinated work of many semionic agents yMay allow the representation of full semiotic processes zIn this sense yWe say that the actions performed by semionic agents are mediated actions – the transformation function is mediated by the evaluation function

Example: Pizza Delivery Organization

What Can we Possibly Do ? zModeling and Simulation of Organizations yMultiples levels of abstraction yFocusing on the resources processed and on the deliverables created zTest and Simulate Multiple Configurations ySimulated re-engineering of organizations yFormal Model in order to better understand the dynamics of an organization zBuild Information Systems yBetter suited to the organizational structure, and which better represent the control demands of organizations

Conclusions zSemionic Networks yAre a potentially interesting tool for the semiotic modeling of organizations zThere is still a lot to do ! yBetter integration of semionic networks to other approaches used in the study of organizations and workflows xWorkflow Management Coalition Standards xEnterprise Distributed Object Computing – OMG-EDOC xOther models of business processes yStudy case of complex real organizations xOnly demos have been generated until now xReal study-cases may suggest new features to be included on the tool yBetter understanding of the semiotic contributions to this kind of modeling