Icmep Rabajante Organizational Sociogram And Chaos Theory

An Analogy of Chaotic System and Organizational Sociogram Jomar Fajardo Rabajante Mathematics Division, Institute of Mathematical Sciences and Physics University of the Philippines Los Baños, Laguna, Philippines

FRAMEWORK CONVERGENCE (Metaphorical)

Social Network Analysis

Chaos Theor

Organizational Network Analysis SOCIOGRAM a visual representation of the social network, and is used to expose patterns in the interaction among actors A

F B

D C

E

Organizational Network Analysis Basic Steps:

context setting and planning survey design survey communication and distribution preliminary analysis and interpretation work analysis communication of results 

Organizational Network Analysis A

Graph Theoretic Analysis

3 5 C

5 B

2 6 5

F

2 3 D

1

Shortest/Geodesic Paths Diameter and Eccentricity Density and Cohesion Index Centrality Indices (e.g. Closeness, Farness and Betweeness) Clusters and Subgroups Emission and Reception degrees of a node Sociometric Status 

4 E

5

Organizational Network Analysis A

Keyplayers:

3 5 C

Trusted Advisors Connectors Bottlenecks Famous Hated 

5 B

2 6 5

F

2 3 D

1

4 E

5

Chaos Theory

Systems Theory

Complexity Theory Chaos Theory

Chaos Theory Chaotic System is nonlinear deterministic (non-random) aperiodic sensitive to initial condition has structure in Phase Space bounded 

Chaos Theory

“orderly disorder” Example: x(n+1) = 3.95 x(n) [1-x(n)]

x(n)

Determinism

n

Chaos Theory  

Sensitivity to Initial Conditions Bifurcation Noah/Joseph effects Feigenbaum number (4.6692...)

Chaos Theory

On what conditions

Perturbation Disequilibrium Co-evolution Edge of Chaos Emergence “Self-organization”

would bifurcation take place?

Chaos Theory Self-Organizing System

Organic Organization Open Organization Learning Organization Flexible Organization 

Transformational Leaders

Chaos Theory Inverted U-Shape Scheme of Management

Chaos Theory Mature Org Members Proactive Capable of managing in “realtime” (continuous adaptation) Capable of giving positive feedback Borrowing from Pareto's Principle: “on the average, 80% of the transformation can be attributed to the works of the 20% of the members”

Chaos Theory

ATTRACTORS  

organization's ID “unified diversity”

Chaos Theory

Fractals patterns that recur at all levels of a system (selfsimilarity)

Chaos Theory

Phase space

Control and Predictability

Chaos Theory   









Nonlinear & Dynamic Aperiodic Sensitive to initial condition Change is exponential Works in a disequilibrium Open System Dissipative



 

 



Deterministic (nonrandom) Bounded Has structuure in phase space Has attractor/s Route to Chaos can be seen Can be in fractal form

Chaos Theory

Control and Predictability

Short-term forecasting can be done (but not long-term). It is occasionally possible to drive a system out of or in to Chaos by applying effective perturbance or changing the initial condition. It is possible to create or destroy an attractor. Creating an environment suitable for continuous adaptation is a proactive way to handle Chaos. Chaos is better than randomness.

THE CONVERGENCE Incorporating the concepts of Chaos theory to the organizational network analysis Creation of sociogram is deterministic, i.e., the connections among the nodes are not randomly generated But social network can have high entropy, which can be initially seen from the values of the diameter, eccentricity, density, cohesion index, and relative entropy/variation in each of the sociometric indices 

THE CONVERGENCE Transitivity can be an indicator of the level of bureaucracy in an organization Nodes having high emission degree, reception degree, sociometric status and centrality index can be the possible keyplayer-attractors Nodes with high emission degree can be the “bottlenecks” Nodes with high emission degree can be the “trusted advisors” Nodes with high betweeness index can be the “connectors” Low centrality index may mean that there is no keyplayer-attractor 

THE CONVERGENCE The candidates for being keyplayers are the possible members of the “20%” who can deliver 80% of the transformation (Pareto's principle) If there is no keplayer-attactor, possibly there is a non-human attractor, or we can introduce one It is usually better to develop non-human attractors, since people are more temporary It is better that the attractor is a fractal (e.g. cultural similarities; mission-vision is well evangelized to the members) The initial perception of a disorganized network can turn out to be organized due to fractal formation 

THE CONVERGENCE What are the possible initial conditions that can drastically change the organizational network?  What other points-of-view can we consider? (as in looking with phase spaces)  Are we comfortable with the status quo or do we want organizational evolution?  Are we capable of self-organizing? Are we trained to manage in “real-time”?  What positive feedback can members give so that we can make it as our new input as initial condition?  It is mostly impossible to predict the future, but it is possible to plant the necessary elements that would increase the probability of making the organization grow 

THE CONVERGENCE

CEO

THE CONVERGENCE

?

THE CONVERGENCE

CEO

2nd attractor

An Analogy of Chaotic System and Organizational Sociogram Jomar Fajardo Rabajante Mathematics Division, Institute of Mathematical Sciences and Physics University of the Philippines Los Baños, Laguna, Philippines