Semionics: A Proposal for the Semiotic Modeling of Organizations

Ricardo Ribeiro GudwinDCA-FEEC-UNICAMP

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

Diadic Model of the Sign

Signifier

Signified

Expression Plane

Content Plane

Triadic Model of the Sign

Sign (Representation)

Object (Reference)

Interpretant (Effect of the Sign)

Semiotics x Semionics

Sign Interpretant

Object

Semiotics x Semionics

Interpreter(Semionic Agent)

Sign(Signlet)

Interpretant(Signlet)

Object

R1

(e.g. symbolic)R2

(e.g. iconic)

Exosemiotics and Endosemiotics

Interpreter(Semionic Agent)

Sign(Signlet)

Interpretant(Signlet)

Internally

Exosemiotic View

Endosemiotic View

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

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

Semionic Network: Action

Signlet(sign)

Signlet(interpretant)

Semionic Agent(micro-interpreter)

Semionic Network: Decision

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Two Tasks Decision

Choose which sign it is going to interpretDecide what is going to happen to it (preserved or not)

ActionTurn it into an interpretant

Decision Evaluation Phase Attribution Phase

Action Assimilation Phase Generation Phase

Semionic Agent

Signlets

Split into compartments Organized into classes, according to compartment types

Data or Function

Signlet

Semionic Agents are Signlets

Compartments Sensors Effectors Internal states Mediated Transformation Functions

Evaluation Transformation

S1 E1 I1eval

S2 E2 I2 I3perform

F1eval perform

F2

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

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

Evaluation Phase

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SHARE ?

DESTROY ?

F1 ??

F2 ??

Fn ????

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??

Semionic Agent

Signlets

WHICH F ?

Evaluation 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

Attribution Phase

Depending on the Access Mode Read: Get a reference to a Signlet, so it can have

access to its internal contentIn 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 itIn 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

Assimilation 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 itAny kind of transformation function can be applied in

order to generate new information Send it to outputs

Signlets are sent to their corresponding outputs

Generation Phase

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

SNToolkit – The Semionic Networks Toolkit

SNToolkit – The Semionic Networks Toolkit

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)

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…

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

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

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

Example: Pizza Delivery Organization

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

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