cas & major current management challenges

Shahin A. Shayan

“Complex Adaptive Systems” Management of Enterprise

Diversity, Uncertainty and Innovation

Source: Complexity in the Workplace - The Sandler Group. www.sandlergroup.net

Shahin A. Shayan

“Complex Adaptive Systems” Management of Enterprise

Diversity, Uncertainty and Innovation1 Shahin A. Shayan2

June 2016 Abstract

Modern international enterprise management objectives have focused on the incorporation of joint economic and social value proposals with the highest possible impacts. Multinational corporations such as Nestle, Wal-Mart, Novartis, Novo Nordisk and Coca Cola have redesigned their corporate strategies towards more complex, multidimensional visions and goals, incorporating high local and regional economic and social value contents. In addition, executive managers have become concerned about the sustainability, stability, continuity, resiliency and growth of these value proposals. This has led to the incorporation of enterprise risk management concepts as an integral part of the management decision making process. The higher the risks an enterprise accepts, the more volatile and less stable and sustainable its value proposals will be. Therefore, an optimum enterprise management effort must incorporate a practical balance between the intended joint economic and social value proposals and the management of the risks and uncertainties inherent in these proposals.

Diversification of goals, objectives, investments, operations and the workforce not only help the enterprise reduce its potential risks and volatilities, it can trigger innovation, creativity and develop new frontiers through possible synergies and joint values that get created through this process. Diversity will cost the management additional headaches due to the created complexities. It also creates the benefits of adaptability, stability, continuity, and new ventures due to resulting innovations. This is clearly easier said than done.

The modern enterprise management challenge is to dynamically manage a complex, adaptive, international, multi-economic/social goal oriented, diversified enterprise, such that the risks and uncertainties inherent in the ventures are contained and the intended joint economic and social values are create in a sustainable and continuous manner.

To understand the dynamics of how these challenges prevail, we have analyzed the related issues through a "Complex Adaptive Systems"3 approach where an interacting multi-agent system such as an international corporation can present learning, developing and innovative dynamics with strong resiliency features. This is how successful, growing and resilient organizations have behaved and survived. Key words: Complicated System, Multi-Agent System, Complex Adaptive System, Diversity, Resiliency, Adaptability, Continuity, Heterogeneity, Mutuality, Nonlinearity, Synergy, Homeostasis, Replicating, Saltation, Rigidity, Entropy

1 The paper was written based on 32 years of experience in financial and economic modeling efforts with 20 years spent at the executive levels of global international financial institutions. Fifteen years of teaching financial engineering to financial executives, university graduate students and searching for mathematical or rule based models to best simulate the real world economic and financial problems triggered writing this paper even further. https://www.linkedin.com/in/shahin-a-shayan-77217114 2 Freelance Management Advisor. 3 A "Complex Adaptive System" or CAS is a complex macroscopic collection of relatively similar and partially connected micro-structures formed in order to adapt to the changing environment and increase its survivability as a macro-structure. They are complex in that they are dynamic networks of interactions, and their relationships are not aggregations of the individual static entities. They are adaptive in that the individual and collective behavior mutate, self-organize and learn corresponding to the change-initiating micro-event or collection of events. https://en.wikipedia.org/wiki/Complex_adaptive_system. See also “Levin A., Simon (2002)” and “Shan, Yin and Yang, Ang (2008)” in the Reference section.

Shahin A. Shayan

General Features of Complex Adaptive Systems (CAS) The modern awakening of interest in Complexity as a science began in Vienna in

1928 with von Bertalanffy's largely descriptive graduate thesis on living organisms as systems.

More than 20 years ago, a group of thinkers, scientists and innovators converged at the Santa Fe Institute in New Mexico to participate in an event that sparked the new field of Complex Adaptive Systems (Cowan, Pines and Meltzer, 1994).

As stated by Cowan, Pines & Meltzer (1994), Complex Systems contain many parts which are highly interconnected and interactive and that a large number of such parts are required to produce the functions of truly complex systems with features such as self-organizing, replicating, learning and adapting.

A “Complicated System” is different from a “Complex System”. In a Complicated System parts, elements or agents are not interconnected or do possess low degrees of interconnections where in a Complex System you have high degree of interconnections. In highly Complex Systems the interconnections are also highly nonlinear. Complexity is a unique and deep property of a system, whereas complication is not (Miller, Page, 2007).

The study of Complex Adaptive Systems or briefly CAS focuses on complex, emergent, macroscopic and holistic properties of such systems. One of the pioneers in the field, John H. Holland a professor of Computer Science & Engineering at the University of Michigan states that CAS has large number of components, often called agents that interact, adapt and learn (Holland, 2006).

In such systems, there is certain amount of mutuality in the agent interactions (positive and negative feedback or feed forward effects). The agents, subparts or elements could be atoms, simple molecules, monomer molecules, complex molecules, cells, neurons, organs, humans, families, enterprises, societies, countries and so on. What distinguishes a CAS from a pure Multi-Agent System (MAS) is the focus on top-level properties and features like self-similarity, complexity, emergence and self-organization. A MAS is composed of multiple interacting agents; whereas in CAS, the agents as well as the system learn and are adaptive. Complex Adaptive Systems are characterized by a high degree of adaptive capacity or homeostasis, giving them resilience in the face of external perturbations and shocks.

Shahin A. Shayan

Other important CAS properties are communication, cooperation, specialization, and reproduction of parts, elements or agents. They can be found on all levels: cells specialize, adapt and reproduce themselves just like larger organisms and even organizations. Communication, coevolution and cooperation take place on all levels, from the agent to the collective system level. The forces driving cooperation, synergy, contagion effects between agents, are difficult to quantify but in many cases, can be analyzed using inductive, computational rule or agent based modeling methods. Axiomatic or mathematical modeling tools are not very effective when analyzing CAS behavior due to the predetermined rigid assumptions ingrained in these models.

Typical examples of CAS include: the global macroeconomic network within a country or group of countries; stock market and complex web of cross border holding companies; social insect colonies (e.g. ants and bees), the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses, and any human social group-based endeavors in a particular ideology and social system such as political parties, communities, geopolitical organizations, war, and terrorist networks of both hierarchical and leaderless nature. The internet and cyberspace, composed, collaborated, and managed by a complex mix of human/computer interactions, is also regarded as a Complex Adaptive System (Wikipedia 2016).

All social systems and organizations can exhibit CAS behavior. Social agents, whether they are bees, people, robots or organizations, find themselves interacting in a web of connections with one another and, through a variety of adaptive processes. These connections can be simple and stable, such as those that bind together a traditional family or complicated and ever changing, such as those that link traders in the marketplace (Miller, Page, 2007). An interesting feature of social agents is that they are capable of thought and hence possess the ability to initiate change independently. This is an interesting and additional element that can add to the degree of complexity of a social CAS.

The remarkable thing about social world is how quickly agent interactions and changes can lead to complex behavior. Social agents do predict, react and adapt to the actions and predictions of other agents. This can be thought of as having high degrees of + or - nonlinear feedback/feed forward effects on one another. The various connections inherent in social systems exacerbate these actions as agents become closely coupled to

Shahin A. Shayan

one another. The result of such a system is that agent interactions become highly nonlinear, myopic or coevolving, leading to the agent’s continuous rule changes through positive and negative feedback/feed forward information exchange process causing the system behavior to become difficult to decouple or decompose for analysis (Miller, Page, 2007). When we consider the potential contagion effects of interacting social agents in highly nonlinear fashion, system dynamics through time becomes complex and very interesting with full of synergies, adaptation, replication and emergence behavior.

The following picture shows a complex network of interacting and communicating colony of fish agents with constantly changing, emergent and innovative collective behavior in order to be resilient and survive against potential attacks and external shocks. We see similar behaviors in bees, ants, birds and other social agents in nature.

Source: A Brief Description of Complex Adaptive Systems. integral-options.blogspot.com

There is a difference between learning and innovation. In learning, we are adapting to be able to recognize an existing set of patterns, which are usually spelled out for us by the environment, whereas with innovation we may suddenly view things differently.

Shahin A. Shayan

Saltation or the theory of evolution by leaps and bounds rather than gradualism, is a viable theory of evolution that can be used to describe the Innovation phenomena in Complex Adaptive Systems (Cowan, Pines, Meltzer, 1994).

Higher risk or information uncertainty in an enterprise is interpreted as having higher system Entropy4 (using the modern microscopic statistical mechanics interpretation of the 2nd law of thermodynamics). Therefore, managing risk and information uncertainties of an enterprise can be viewed as managing and containing its Entropy. If we do not manage a system and let it go on forever, the information uncertainty increases which can lead to higher risk and entropy. One must manage the information uncertainty of a system to contain its risks and entropy. Information uncertainty can exist at an agent level all the way up to the system, collectively.

Managed learning and innovation in an enterprise can reduce information uncertainties, risks and Entropy (Cowan, Pines, Meltzer, 1994). An unmanaged complex system is in a high state of Entropy and as it learns and adapts, it moves towards a lower state of Entropy. In managing a CAS one must always try to maintain the Entropy levels at the desired objectives. Competition usually increases complexity which if properly managed can lead to more innovation and resilient behaviors (Cowan, Pines, Meltzer, 1994).

To use CAS concepts in managing organizational Diversity, Uncertainty and Innovations we must pay attention to the following important CAS characteristics:

They have many parts, pieces, elements or agents. The agents interact dynamically, and the interactions can be physical or involve the

exchange of information only. Such interactions are rich, i.e. any element or sub-system in the system is affected by and

affects several other elements or sub-systems simultaneously. The interactions are non-linear: small changes in inputs, physical interactions or stimuli

can cause large effects or very significant changes in system behavior. Interactions are primarily but not exclusively with immediate neighbors and the nature of

the influence is modulated. There is an emphasis on collective behavior of the agents or parts.

4 “Entropy” is a measure of the number of microscopic configurations that correspond to a thermodynamic system in a state specified by certain macroscopic variables. In the modern microscopic statistical mechanics interpretation, Entropy is the amount of additional information needed to specify the exact physical state of a system, given its thermodynamic specification. Understanding the role of Entropy in various processes requires an understanding of how and why the information changes as the system evolves from its initial to its final condition. It is often said that Entropy is an expression of the disorder, or randomness of a system or of our lack of information about it. Entropy can be considered as a measure of unpredictability of information content or risk of a system. https://en.wikipedia.org/wiki/Entropy

Shahin A. Shayan

The system shows learning or adaptive properties collectively which makes the agent or the system more likely to survive, be resilient, reproduce and become sustainable.

System behaviors can result in emergent, new and innovative collective behaviors and if not managed properly can hinder it.

Filtering out noise from information as input, is a part of coarse graining, learning and identifying correct patterns and regularities in the system.

Any interaction can feed back onto itself directly or after a number of intervening stages. Such feedbacks can vary in quality. This is known as the recurrence effect.

It is usually very difficult or impossible to define the system boundaries. They are open systems.

They operate in a state of dynamic, continuous and changing equilibrium. These systems have a history. They evolve and their past is co-responsible for their

present and future behavior. There is a nonlinear correlation between the past, present and the future. This can be interpreted as having memory ingrained in the structure of the system.

Elements in the system are usually ignorant of the behavior of the system as a whole, responding only to the information or physical stimuli available to them locally.

In summary, the main features of Simple (SS), Complicated (CS), Multi Agent (MAS) and Complex Adaptive Systems (CAS) are compared in Table 1.

Table 1 “Complexity Metrics”5

"Main features of Simple, Complicated, Multi Agent and Complex Adaptive Systems"

Features System Type SS CS MAS CAS

# of Agents, Parts, Pieces or Elements L L to M M to H M to H

Degree of Heterogeneity or Diversity of Agents L L L to M M to H

System’s Degrees of Freedom L L L to M M to H System’s Entropy, Information

Uncertainty or Risk L L L to M M to H Mutuality, Interconnections, Communications or Multiple Agents +/- Feed Back/Feed

Forward Effects N L L to M M to H

Degree of Nonlinearity of Agents Interactions N L L M to H

5 This table was put together using the experiences described in footnote 1.

Shahin A. Shayan

Synergy, Contagion Effect, Coupling or the Degree of Agents

Cooperation N N N M to H

Homeostasis, Adaptation, Self- Organization or Agents Learning

Behavior N N N M to H

Replicating or Reproduction Behavior N N N M to H

Saltation, Emergence, Novel or Innovative Behavior N N N M to H

System’s Rigidity to Change H H M L System’s Equilibrium Static/Stable Static/Stable Static/Stable Changing/Dynamic

Note 1: SS = Simple System, CS = Complicated System, MAS = Multi Agent System, CAS = Complex Adaptive System Note 2: L = Low, M = Medium, H = High, N = None Managing Enterprise Diversity, Uncertainty and Innovation

With globalization, increased global population awareness, free market emphasis, increased competition, information, communication, internet and social network impacts, the business world has gotten very complex. Not just complicated, but unpredictable and often overwhelming, with lots of moving parts and volatility of perspectives and opinions of the agents involved. This is even more so for multi-national multi-functional business conglomerates such as Nestle, Unilever, Novartis, Coca Cola and … etc. They function in many countries, deal with diversity of products, markets, consumers, cultures, regulatory regimes, multi-dimensional uncertainties and risks.

An important theme in managing an international enterprise is the determination of where it stands on the simple to complex scale. Based on the “Complexity Metrics” described in Table1, we should be able to do this with relative ease. Organizations need to determine the proper quantitative measures for each of the complexity features described in this Table.

Obviously the agent-agent forces driving nonlinearity, cooperation, synergy, and contagion effects are difficult to quantify but in some cases, can be analyzed using inductive analytical methods, game theory, computational rules and agent based simulation models combined with statistical approximation methods. Strategically, a simple rule for an enterprise will be to use expert judgments to rate each of the

Shahin A. Shayan

complexity features defined in Table 1 and then determine the overall complexity level of the organization.

A domestic, uni-functional, hierarchical based, centrally managed, limited and small organization will probably be rated more towards simple and a multinational, multi-functional, network based, decentralized, large conglomerate will be rated more towards a complex adaptive structure.

As stated before, the various agent-agent connections inherent in social systems or business enterprises exacerbate complexity levels as agents coevolve and become closely coupled to one another. The result of such enterprises, if controlled and managed properly, will be a complex dynamic evolution through time that will be very interesting with full of synergies, adaptation, replication, emergence and innovative behaviors. Complexity in an enterprise can both drive innovation and if not managed properly create confusion, noise and failure.

Emergence is generally considered to be a process that leads to the appearance of structure not directly described by the defining constraints and forces that control the system or enterprise. In emergence localized behavior of agents aggregates into global behavior that is, in some sense, disconnected from its origin. Emergence is not imposed by a central force or management process, but results from an interactive rule based structure between agents, affecting the system at various levels. Over time, emergence behavior is directly related to innovation which leads to new structures, leading to systems that are more resilient and adaptable (Miller, Page, 2007).

Managing risk and uncertainty is a central part of all enterprise strategies. Corporate reputation and credibility that takes decades to build up can be ruined in an hour through incidents such as corruption scandals or environmental accidents. These events can also draw unwanted attention from regulators, courts, governments, public and media. Building a genuine culture of 'doing the right thing' and be responsible towards stakeholder’s interest within an enterprise can offset these risks (Kytle, Ruggle, 2005).

Based on CAS modeling concepts, properly managed enterprise diversity in terms of number of agents, nature of feedback and feed forward relationships among agents, organizational goals, objectives, investments, operations and the workforce not only help the enterprise reduce its potential uncertainties and risks, it can trigger innovation, creativity and develop new frontiers through possible synergies and joint values that get

Shahin A. Shayan

created through this process. Diversity will cost the management an additional headache due to the created complexities and if managed properly can create the benefits of stability, continuity, and new ventures due to resulting innovations. This is clearly easier said than done.

Using the CAS analysis approach, top management of multi-national business enterprises must model their operations based on multi-agent interacting pieces of functional and geographic operations. These pieces must be diverse and provide their own value proposals (they could be SBUs or divisions). The relationship between these pieces must be managed in a synergistic, dynamic and interactive way with multiple positive/negative feedback, feed forward relationships created among the business parts. Network based interactions between different operations with a lot of brain storming sessions and simple/understandable rules to control the process will be required. It is during this stage that information uncertainty and risks can increase and if not managed properly, a lot of noise can creep into the operations and destructive effects can slow down the business. Through proper management of the information, risks and system entropy, saltation, emergence and innovative ideas, products, processes and concepts with regards to how to run the business parts and the overall system can be expected. This is when you will observe homeostasis, adaptation, self-organization or system learning behavior become prevalent. Replicating or reproduction behavior will also appear. At this stage, one can expect having an enterprise that is resilient and continuously adjusting itself to external shocks and stays in a state of dynamic equilibrium.

Based on the above analysis and CAS features from Table 1, the main characteristics of a dynamically managed complex, adaptive, international, multi-economic/social goal oriented, diversified enterprise, such that risks and uncertainties inherent in the ventures are contained and the intended joint economic and social values are create in a sustainable and continuous manner have been summarized in Table 2.

Shahin A. Shayan

Table 2 "Main features of a dynamically managed Complex Adaptive Enterprise"

Features How to manage dynamic CAS features # of Agents, Parts, Pieces or

Elements Manage at medium levels. High levels will lead to loss of control Degree of Heterogeneity or

Diversity of Agents Manage and emphasis diversity of goals, objectives, functions,

investments and work force. Too much diversity and heterogeneity leads to uncontrollable noise which can lead to instability and chaos

System’s Degrees of Freedom Manage at medium levels. Do not let this go out of control or you will face chaotic behaviors

System’s Entropy, Information Uncertainty or Risk Manage at low to medium levels

Mutuality, Interconnections, Communications or Multiple Agents +/- Feed Back/Feed

Forward Effects

Manage at medium to high levels. Organized, policy driven, effective and conductive communications and feedbacks between business units,

personnel and functional divisions will help stability and enhance innovation and effective emergence behaviors

Degree of Nonlinearity of Agents Interactions Manage interactions at understandable, open and creative levels

Synergy, Contagion Effect, Coupling or the Degree of Agents

Cooperation Manage and emphasize team work, synergies through organized

committees and various interacting functional silos Homeostasis, Adaptation, Self -

Organization or Agents Learning Behavior

Manage and emphasize new idea generations and education at all levels

Replicating or Reproduction Behavior

Manage and emphasize new functions, ventures and businesses that complement the existing value creation efforts

Saltation, Emergence, Novel or Innovative Behavior

Manage and emphasize emergent and innovative thoughts and functions through brain storming sessions and take calculated and managed risks

System’s Rigidity to Change Manage and maintain at low levels System’s Equilibrium Manage and emphasize a dynamically changing state. Never emphasize

static behavior. Manage equilibrium positions on a continuous basis Note 1: SS = Simple System, CS = Complicated System, MAS = Multi Agent System, CAS = Complex Adaptive System Note 2: L = Low, M = Medium, H = High, N = None

Conclusion Global business environment is changing fast. It is in a state of continuous flux and

changing equilibrium. To survive and be resilient, modern international enterprises must stay in similar state of flux and manage the enterprise equilibrium, dynamically.

Shahin A. Shayan

The traditional, static, functional, rigid organizational models that are based on certain utopian behavioral assumptions of organizational agents do not provide the necessary management tools towards this objective. They lead to rigid, non-adaptive, static based organizational models that fail in a dynamically changing business environment.

The challenge is to use appropriate models so to dynamically manage a complex, adaptive, international, multi-economic/social goal oriented, diversified enterprise such that the risks and uncertainties inherent in such operations are contained and the intended joint economic and social values are create in a sustainable, continuous and resilient manner. In this paper, we have suggested that using a “Complex Adaptive System” or in brief CAS approach in managing an enterprise is an effective and viable model for such a purpose. Managing an international enterprise based on features in Table 2 was proposed as a viable alternative.

Using the CAS modeling approach led us to propose that a managed diversification of goals, objectives, investments, operations and the workforce not only reduces the enterprise potential risks and volatilities, but it also triggers innovation, creativity and develops new frontiers through possible synergies and joint values that do get created.

When we look at the enterprise on a holistic and collective basis, CAS modeling concludes that managed diversity will cost the management additional headaches due to created complexities while it leads to new products, markets and ventures due to resulting innovations providing the adaptability, stability, continuity and needed benefits of resiliency in a dynamically changing environment.

Shahin A. Shayan

REFERENCES

Cowan, A. G., Pines D. and Meltzer D. (1994). "Complexity, Metaphors, Models, and Reality". Proceedings Volume XIX, Santa Fe Institute Studies in the Science of Complexity. Addison-Wesley Publishing Company. Reading, MA.

Holland, H. J. (2006). "Studying Complex Adaptive Systems”. Journal of system sciences & complexity, 2006 19: 1-8. Springer Science + Business Media, Inc.

Levin, A. S. (2002), "Complex Adaptive Systems: Exploring the known, the unknown and the unknowable". American Mathematical Society Bulletin, Volume 40, Number 1, Page 3-19.

Miller, J. H. and Page S. E. (2007), "Complex Adaptive Systems: An Introduction to

Computation Models of Social Life". Princeton University Press. Princeton, New Jersey.

Shan, Y. and Yang, A. (2008), "Applications of Complex Adaptive Systems". IGI Publishing. Hershey, PA.

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