1.       Full​ ​Bibliographic​ ​Reference

 

Bertalanffy, ​ ​L. ​​V.​ ​(1950). ​​An​ ​Outline​ ​Of​ ​General​ ​System​ ​Theory. ​ The​​British​​Journal​​for​​the​​Philosophy​​of

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Science​, ​ I(2),​ ​ 134-165.​

 

2.       Introduction: ​ ​Objectives, ​ ​Article​ ​Domain, ​ ​Audience,​ ​Journal​ ​and​ ​Conceptual/Empirical​ ​Classification

Introduction:

The main objective of this paper is to involve history and perspective of modern science which explains building blocks of reality. This paper generalize ideas about how science has assumed  in past and how modern science is being considered corresponding to various branches of science as  physics, technology, biology, philosophy, social science and different fields.[1] The author describes many physics and thermodynamics laws and explains it with examples as it’s the highest level of science we have which can be applied as well as interrelated to other branches of science.  The second part presents a conception of the equifinality which explains characteristic of self-organized system.  This paper explains that how we can isolate a framework in to parts and by studying it we can get thought of unity of science.

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As general system theory is general it considers various frameworks that interlinked with other systems. It could be baseline knowledge for researchers to study biology, mathematics, physics, social-science or any other topic.[2] This article’s main audience would be the system theorist interested in all-encompassing view of science and technology.  Also, the students could use as research or analytical tool in innumerable academic areas as systems science, centered in information technology, cybernetics, automation, systems engineering and related fields.[3] Apart from this, people who are interested in new technologies can also refer this paper, but they should be familiar with the concepts like reductionism, emergent property, isomorphic law, second law of thermodynamics, laplacean spirit and many more. These scientific laws explain the domain of the paper and author’s point of view and can be helpful for further research.

This paper is conceptual as it did not mention any detailed analysis of the evidence. But it provided some of the real-time examples regarding the topic by describing the different laws and system. The advantages of the conceptual framework are-it prioritize content, promotes purposeful learning and it will allow the readers to make educated. [3]A conceptual system capable of stating and supporting science and innovation for all should be progressed decisions.

3. Summary

The problem addressed in the article is need for formal correspondence of general principle, irrespective of how structure of an organization and function of their component works with rapid change in innovation, science, human conduct, also, social organization. [4]

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System theory can solve most of the issues that are mentioned above. The primary issue can be solved using general systems theory is a fundamental body of knowledge which could be really helpful in the effort to describe the nature of any system.  It promotes an interdisciplinary science across a universal application with a various ideas and laws. In order to solve problems, make suggestions and predict the future, system theorist utilizes speculations, models and ideas from the immense region of system science.[5] It is a subject in the philosophy of science worried about acknowledgment of parallel methodologies or strategies in different disciplines. It states the openness of system way to adapt to the new realities. Moreover, A system might possibly achieve a final state in various ways. Equifinality is describing as goal-seeking myriad of system. These are the ways General Systems theory characterize any system into more straightforward terms.[6]

This solution is appropriate because this theory has been solving many issues as mentioned above and it can be used across several systems like medical, physics, social-science, computer technology, phycology by developing the way for new fundamental models.

4. Results

 

As result of this paper it provides brief idea and knowledge about general system theory.  System theory viewpoint and concept witnessed the emergence and complexity of system where numerous subsystems are cooperating with larger systems. [7]Conceptual boundaries of paper describes inter- reliance of phenomenon- physical, natural, information theory, biology, Psychology, social and economic. Using system theory, we can ponder complex variable impacting one another. Also, one can able to predict expected happening of system in future. It explains dynamic behavior of system and unity of science.[8]

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5. Contribution

Bertallanfy is known as the founder of systems theory. His research paper has increased the development of the different kinds of approaches in systems like game theory, systems analysis, information theory, cybernetics etc.,

Nature of living systems in the open system has broadened the scope of Cybernetics. So, this concept of open system which is the base for research in the field of non-equilibrium thermodynamics can be considered as the important contribution. His General System Theory provides a different kind of method which focusses on the system’s structure rather than on system’s function.

To all the sciences it has provided a kind of universal which is useful for removing the duplicate theories that have been developed by people in one field who are not aware of the theories that has already been developed by others in a different field of science. In this paper, we can also the concepts of Isomorphism.

We can also see the way how the systems theory has been evolved to the communications theory. It provides us information about how the Isomorphism is responsible for the unity of science. We can see that the author has provided us information about how different concepts can be associated to one theory.

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Also, the Classical Science is broken down into elements to understand the concepts in a better way by comparing it with modern science. He has provided the existence of the concepts tin several fields. He also provided various information about the Isomorphic laws which came into existence. He has explain ed the concepts on open, closed and feedback systems.[8]

 

 

6. Foundation

 

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Gestalt

The principle of gestalt was first proposed in philosophy and psychology in 1890 by Christian von Ehrenfels. It suggests that mind forms a global whole with self-organizing tendencies. It also means that human try to interpret the object in the simplest way.[9]

Boltzmann Derivation

The Boltzmann equation states the statistical behavior of a thermodynamic system not in a state of equilibrium.It also describes “the average distribution of non-interacting material particles over various energy states in thermal equilibrium, and is applicable when the temperature is high enough or the particle density is low enough to render quantum effects negligible”.[10]

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Teleology

In western philosophy, the term and concept of teleology originated in the writings of Plato and Aristotle. It is a reason for something in function of its end, purpose, or goal.[11]

Nicolai Hartmann’s theory of categories

Nicolai Hartman was one of the leading German philosophers of the first half of the twentieth century. He systematically developed a comprehensive and rich theory of categories, which set out his thought on ontological modalities, the difference between the spheres of real and ideal being, the fundamental categories of reality, the ontological levels in which reality is structured, and cosmological categories.[12]

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Second principle of thermodynamics

The second law of thermodynamics states that “the total entropy of an isolated system can never decrease over time”.Rudolph Clausius and William Thomson founded the First Law of energy and Second Law of Thermodynamics in 1850.[13]

 

Pareto Principle

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This Pareto principle was first introduced in 1908.It states that 80 percent of consequences or effects come from 20 percent of the causes, or an unequal relationship between inputs and outputs. This principle was first introduced in 1908.[14]

 

       Heisenberg relation

Heisenberg’s uncertainty principle is one of the most popular ideas in physics. It says that there is a fundamental restriction to what we know about the behavior of quantum particles. The principle also says that we cannot calculate x (position) and p (momentum) of a particle with absolute precision.[15]

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7. Analysis

• In the areas of metal health, behavioral sciences and social work the study of human behavior was possible with the help of Systems Theory. We can also see that the framework of open-systems to be interconnected, associated and cooperating.

• Bertalanffy has provided the complete information about the concept of general system theory when compared to the other researchers. With the exploration of systems theory hard systems approach, mixed systems approach and soft systems approach have been developed.
• With the development of IS&T we can see that in an organization information flows for successful communication and management as it is considered as an open system. Bertalanffys open system can be considered as well stated and well justified.[16]

 

 

8. General Critique

A retrospective view on Bertalanffy theory of General Systems reveals both striking prescience in his ability to identify commonalities between different fields of science yet overzealous in his predictions of the impact of general systems theory.  Some of the mathematical functions described by Bertalanffy as isomorphic processes have not only become ubiquitous across fields of science but have gained awareness among the general population as well.  For example, what Bertalanffy referred to as Pareto’s Law of the distribution of income in economics and allometric growth in biology has been generalized to the statistical concept of the power-law probability distribution in the sciences and referred to colloquially as the 80-20 rule (where 80% of a measured effect is attributed to 20% of a given cause).

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However, Bertalanffy made strong predictions beyond isomorphic processes that seem to have failed the test of time: “Reality, in the modern conception, appears as a tremendous hierarchical order of organized entities, leading, in a superposition of many levels, from physical and chemical to biological and sociological systems. Unity of Science it granted, not by a utopian reduction of all sciences to physics and chemistry, but by the structural uniformities of the different levels of reality” (Emphasis ours).  Yet just as statisticians have become more adept at identifying structural uniformities, so too have researchers been able to reduce increasingly complex phenomena into their underlying physics and chemistry.  For example, the sensation of pain has been broken down into a series of action potentials which can be measured on an oscilloscope and explained by voltage differentials caused by the relative concentrations of sodium and potassium ions along a nerve axon.  Bertalanffy seems to think of a general systems theory as more useful and unifying than reductionist theory, but it seems that each serves its own unique purpose; the reductionist approach informs us of the underlying machinations of phenomena, where the general systems approach informs us of the trends that phenomena are likely to follow.

 

9.       Issues​ ​(listed​ ​by​ ​the​ ​author)

The performance and behavior of processes within various fields of sciences with their dynamic interactions is not resolved through reductionist approaches to science.  Despite this, researchers across the fields of physics, biology, psychology and medicine focus their efforts on breaking down phenomena into their most elementary units to be analyzed and put into experimentation. But a non-reductive theory is necessary to understand dynamic interactions and emergent behavior where the whole of a phenomena is more than the simple summation of its parts.  A theory of general systems can be used to analyze patterns of phenomena that exhibit similar characteristics across different fields.  This general systems theory can be used to transfer findings and principles of field of study to another in a methodological fashion.

The author describes many issues with the implementation of general system theory in various disciplines. For example isomorphic law can be applied differently across various industries and it is not clear how it would be useful in the other areas. The delays in decision making also could lead to impact the elementary units while applying the general system theory which could result in absence of information across various subunits.

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10.                Issues​ ​(in​ ​your​ ​opinion)

Another issue we identify with general systems theory as described by Bertanaffly are the limitations for researchers to create models that can accurately capture real world behavior with enough accuracy to provide useful predictions.  Unlike hypothesis testing for closed systems in which researchers can isolate variables for experiments, open systems usually have unknown variables.  This means a variable not included in a model may be the most significant factor in real world outcomes.  The reverse is also true; researchers may choose to play high emphasis on certain variables in their models, even though these variables have little impact in the real world.  The general principle of emergent behavior further complicates the creation of models, as the true impact of certain variables may only become apparent with their combination of other variables.  Thus, a known variable may be deemed largely insignificant in a model even when it has large significance in determining emergent behavior.

One issue with general systems theory is that it relies on concepts that can be difficult for average people to understand and appreciate.  The author provides analysis related to several fields of sciences that behave in a similar way. Their complex relationships with each other makes the concept difficult to relate it to real life situations. In many cases the theory has only been mentioned but the method was not specified. Moreover, if the practical implications of the system theory or general theory is not mentioned by author, it is hard for the users to understand the concept or believe the explanations given by author. So to be more impactful, author should have used simple real world examples to explain this concepts.

 

11. Impact

 

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After doing impact analysis, we found this article holds strong impact. This article has been cited 2526 times.  This article has been used in many other references as well as in Bertanlanffy own citations in other papers. Following are some of the articles:

• A systemic and cognitive view on collaborative knowledge building with wikis(Ulrike Cress, Joachim Kimmerle,2008)

• Rethinking a Systems Approach, Peter Checkland.1984
• Systems Theory as the Foundation for Understanding Systems, Kevin MacG. Adams Patrick T. Hester Joseph M. Bradley Thomas J. Meyers, Charles B. Keating,2013
• Heider F. The psychology of interpersonal relations. New York: Wiley, 1958. 322 p. [Department of Psychology. University of Kansas. Lawrence. KS]

• Systems theory, computer simulations and archaeology, James Doran 2010

12. Questions

 

1.In what ways are reductionist and systemic approaches to science mutually exclusive?

2.Can general systems theory inform us about the underlying mechanisms which drive processes, or does it only inform us of processes at a macro-level view?

3.How can open systems be adequately modeled when there are almost always going to be external factors acting upon the system that have not been included in the model?

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4. How can System theory be applied to smaller organizations since this approach does not recognize the

differences in systems
5. How the delays in decision making could be corrected while using Systems General Approach.

 

 

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Additional Sections

 

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1. Isomorphic Examples in Information Technology

Bertanlanffy describes isomorphic processes as phenomena across different fields of science that follow either the same mathematical laws or have striking similarities in their general principles. There are many such isomorphic processes between Information Technology/computer science and other disciplines.

For example, the exponential growth function which describes unlimited growth of animal populations or the accrual of wealth under compound interest also describes Moore’s law, which predicted the growth in the number of transistors in an integrated circuit [Figure A1].  The logistic growth function, used to describe populations growing to a carrying capacity, seems to also apply to cellular phone adoption [Figure A2].

Figure A1

Figure A1[17]Karl Rupp uses research data to plot the growth of CPUs by four different measures.  The transistor count is used to provide evidence for Moore’s law.

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Figure A2

Figure A2[18]

Graphs from the world bank database for statistics related to telecommunications.  Of interest is the graph of mobile cellular subscriptions, which suggest that cell phone adoption is following a logistic growth function.

Numerous phenomena in information technology follow a power-law distribution.  The power-law distribution is a generalization of Pareto’s Law of income distribution, which describes the tendency for large proportions of a nation’s wealth to be held by a small fraction of the population.    The power-law distribution describes multiple aspects of online user activity; it describes how some webpages get disproportionate links and page visitors [Figure A3] and how a few web users tend to generate a disproportionately high amount of content [Figure A4].

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Figure A3

Figure A3[19]

Tracking the tails: The illustration shows the distribution of visitors arriving at YouTube from referring web sites. Figure A3(a) shows that the number of visitors to YouTube is directly proportional to the ordered rank of the referring web site. Figure A3(b) shows that there are a few sites that provide a bulk of the referrals to YouTube. The data was collected from Compete.com, which omitted values for referrals that resulted in less than 3000 visitors arriving at YouTube.

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Figure A4: Probability distribution of activities and degree

Figure A4: Probability distribution of activities and degree[20]

(a) Probability density function of Wikipedia contributors as a function of the number of performed page edits in four languages. (b) Probability density function of news2.ru for five different activities. Lines indicate power-law fitting for Spanish and Stories with the maximum likelihood methods. (c) Probability distribution of degree for social networks as a function of number of links between Wikipedia contributors. Degree represents the number of links other users establish with a given user. (d) Distribution for networks of relationship (positive/negative) between users of news2.ru web portal and users’ friendships.

Muchnik et al. build a probability distribution model based on data on the activity of users of Wikipedia and a news-sharing website news2.ru.  They find that the behaviors of these users follow a power-law distribution, providing further evidence for isomorphic process in IT.

As the previous examples have been based on similarity of mathematical laws, we shall now examine isomorphic processes of similarities of general principles between information technology and biology.  Researchers at [ref] have found that models of data transfer over TCP networks accurately predict the foraging behavior of ant colonies [ref or figure].  Additionally, ant behavior, specifically the production of pheromone trails, have been modeled to create algorithms for the traveling salesman problem [ref/figure]. [21]

The ant colony system (ACS) based algorithm is compared against other algorithms used to find optimal solution to the traveling salesman problem, a genetic algorithm (GA), evolutionary programming (EP) and simulated annealing (SA).  The ACS algorithm can find better solutions with far fewer tours than the other algorithms.  [22]

Dorigo and Gambardella develop an algorithm based on the pheromone trails of ants to generate solutions for the traveling salesman problem.  This algorithm is evidence for the usefulness of the study of isomorphic processes between biology and computer science.

 

2. Evolution of Systems Thinking

Systems theory is the collaborative study of systems. It is a theory which evolved by the works of Norbert Weiner, Ludwig von Bertalanffy and Ross Ashby.  It encloses a broad field of research with divergent abstraction and area of focus.  The paper has many examples where Bertalanffy approach to system theory describes systems interacting with cybernetics, game theory and other fields. Bertalanffy elaborated on the research of open/closed systems and suggested that the law of thermodynamics applies to closed systems as well, but not only to open systems. There have been many advances in system thinking post Bertalanffy thought- below are the developments.

Game Theory: It is the study of decision making which applies in all fields of social science. Game Theory was proposed after John von Neumann published his paper on the Theory of Games of Strategy. He used Brower’s fixed-point theorem on continuous mappings into compact convex sets which turned into a standard technique in game theory and mathematical economics.[23]

Cybernetics:  Cybernetics is an approach on feedback mechanisms and emphasizes on the requirements and the outcomes through which a system being engaged creates a closed signaling loop which in turn has a change in its environment resulting in a change reflecting onto the system. Fields of study which have been impacted by cybernetics include system theory, organizational theory, cognitive psychology etc. The fundamental objective of the wide field of cybernetics is to comprehend and characterize the capacities and procedures of frameworks that have objectives and that take an interest in roundabout, causal chains that move from activity to detecting to correlation with wanted objective, and again to activity.

Decision theory:  Decision theory is the study related with choices of individual agents. It is almost like game theory- the only difference being decision theory related to choices of individual agents while game theory is related with interactions of agents whose decisions affect each other. There have been several researches on what type of alternatives can be considered. The alternatives would be probability theory, Ludic fallacy etc. Heuristics is another field in decision theory where it is based on routine thinking but it could present the danger for errors and misrepresentations which might be effectively refuted in a well-ordered procedure for reasoning. [24]

Complex adaptive systems:  Complex adaptive systems are subject to positive feedback and are made up of complex interconnected components. They are versatile and can change and gain from experience. It is also based on a theory of Agent based Modeling which consists of agents communicating with each other in an unfrivolous manner. By communicating with each other in such a manner, the emergent behavior of such systems can be known.[25]

Factor Analysis: Factor Analysis is a measurable data reduction and investigation system that endeavors to clarify correlations among different results as the after effect of at least one fundamental factor. The system includes data reduction as it tries to constitute a set of variables by a modest number.

3. What is the relevance of systems theory with the Artificial Intelligence/ Machine Learning research? Can system science be compatible with the computational theory of mind? If so, provide sufficient arguments. (Include supporting bibliography.)

The concept of systems theory was founded by biologist Ludwig Bertalanffy. One of the biggest views of systems theory is viewing a cluster as its own scheme with several moving components that influence one another. A system is characterized by a set of parts that interact to shape a coherent whole. Structures have distinct boundaries separating them from outside factors and distinguishing among inputs, or factors that impact the system and outputs, or results and products of the system. Systems may additionally have feedback loops, which arise when outputs of a system go back as inputs, forming a circuit. Modifications in one aspect of a system will affect other additives in addition to the overall entity. This dynamic makes it possible to predict what would possibly take place while a system reviews a known change. Von Bertalanffy (1956) defines a system as a complex of interacting elements. On other end, Artificial Intelligence can be related to Bertalanffy concept that system theory plays a predominant role within the development of latest systems. System theory permits different professionals like scientists, engineers to develop conceptions from an abstracted state which will be made real through developments of its core models and principles. [26, 27]

An AI could be computing system that’s ready to perform tasks that usually needs human intelligence. These systems are powered by machine learning and some them by deep learning. One can say that AI is simply combination of multiple systems focused on problems and machine learning is all about inputs of machines via sensors and turn it to output which is often complex. Therefore, systems theory can help to combine these systems together more efficiently. However, both system theory and machine learning can be found in the umbrella of complex systems.[28]

The computational theory of mind can be understood as a software program of the brain.  CTM is one of the most important concepts in current research of theories of artificial intelligence, decision making etc. The system science can be compatible with the computational theory of mind. Many scientists and biologists have made assumptions that mind is a set of processes and the brain is a machine. There are many phenomena that are said to be attributes of the mind like thinking, imagination, emotions etc. which can be explained in the form of system science.

The procedure power and software system engineering tools currently exist to build

experimental models of the brain that check theoretical predictions against determined

performance in a real-world environment. There are sensible reasons to believe that a scientific

rationalization of mind in terms of neuronic activities within the brain is possible among the

predictable future.

System science is the study of the nature of any system (Simple or complex) and it is also used to solve problems. The computational theory of mind says that mind is a computer and it works based on information processing system. So it can be assumed that to prove computational theory of mind, one will need system science principles to solve.

As per hypothesis, there are different logics explained in scientific terms for all those happening in the brain. For example, imagination is something or process that our brain visualizes about objects, situations etc. Thinking is again the process of analyzing the results. Understanding is the thing that happens when the brain’s internal portrayal of outer reality is satisfactory for producing intelligent behavior. Awareness is a condition where human brain understands the flow, system and its surrounding environment. Thus, all the functional process in the brain have computational equivalents.[29]

 

13. Annotated Bibliography

1. Bertalanffy, L.v., September 19, 1901 – June 12, 1972).

2. Drack, M.J.J.o.E.Z.P.B.M. and D. Evolution, Ludwig von Bertalanffy’s organismic view on the theory of evolution. 2015. 324(2): p. 77-90.

3. Ramosaj, B. and G. Berisha, Systems theory and systems approach to leadership. 2014.

4. Lars Skyttner (University of Gavle, S., General Systems Theory Problems, Perspectives, Practice. 2006..

6. Rapoport, A., General system theory: essential concepts & applications. Vol. 10. 1986: CRC Press.

7. Mitchell, G., BERTALANFFY’S GENERAL SYSTEMS THEORY.

8. Bertalanffy, L.v., General system theory: Foundations, development, applications. 1969.

9. Turner, R., https://study.com/academy/lesson/gestalt-psychology-definition-principles-quiz.html.

10. Wikipedia, https://en.wikipedia.org/wiki/Boltzmann%27s_entropy_formula. 18 May 2008

11. Wikipedia, https://en.wikipedia.org/wiki/Teleology. 28 April 2017

12. Hartmann, N., https://plato.stanford.edu/entries/nicolai-hartmann/. Jul 1, 2012.

13. Wikipedia, https://en.wikipedia.org/wiki/Second_law_of_thermodynamics. 24 October 2002.

14. Investopedia, https://www.investopedia.com/terms/p/paretoprinciple.asp.

15. Jha, A., https://www.theguardian.com/science/2013/nov/10/what-is-heisenbergs-uncertainty-principle. Nov 2013.

16. The Quest for a General System Theory. 1968.

17. Rupp, K., et al., Years of Microprocessor Trend Data. 6.

18. “Mobile cellular subscriptions,” World Bank Group. . 10-Sep-2018.

19. Mahanti, A., et al., A tale of the tails: Power-laws in internet measurements. 2013. 27(1): p. 59-64.

20. Muchnik, L., et al., Origins of power-law degree distribution in the heterogeneity of human activity in social networks. 2013. 3: p. 1783.

21. Carey, B., “Stanford biologist and computer scientist discover the ‘anternet’,” Stanford School of Engineering, . 12-Jul-2018.

22. Dorigo, M. and L.M.J.I.T.o.e.c. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem. 1997. 1(1): p. 53-66.

23. A. V. Vasilakos, R.K., E. Hossain and H. Kintis, Special Issue on Game Theory. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), June 2010. vol. 40, no. 3, pp. 554-558.

24. North, D.W., A Tutorial Introduction to Decision Theory. IEEE Transactions on Systems Science and Cybernetics, Sept. 1968. vol. 4, no. 3, pp. 200-210.

25. Wildberger, A.M., Complex adaptive systems: concepts and power industry applications. IEEE Control Systems Magazine, Dec. 1997. vol. 17, no. 6, pp. 77-88.

26. F. Heylighen, C.J., What is Systems Theory? Cambridge Dictionary of Philosophy, 1992.

27. Adams, K.M., Hester, P. T., & Bradley, A historical perspective of systems theory. Industrial and Systems Engineering Research Conference. 2013.

28. Cristina Mele, J.P., Francesco Polese, A Brief Review of Systems Theories and Their Managerial

Applications. Institute for Operations Research and the Management Sciences, 2018.

29. Albus, J., Toward a

Computational Theory of Mind. Albus – Journal of Mind Theory. Vol. 0 No. 1.

 

Description of the references used

• Drack, M.J.J.o.E.Z.P.B.M. and D. Evolution, Ludwig von Bertalanffy’s organismic view on the theory of evolution. 2015. 324(2): p. URL: https://www.ncbi.nlm.nih.gov/pubmed/25727202

This paper discusses about the organismic view on system theory evolution. This is followed by Bertalanffy’s considerations on the theory of evolution.

This book  describe the connections between the different parts of a system in the creative process. Learning about this process helps regulate future creative work. An organization can consider and maintain a culture which is conducive to creativity in open system.

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• General system theory : essential concepts & applications / Anatol Rapoport-1998

URL:https://www.researchgate.net/publication/287319297_Cybernetics_from_Past_to_Future

This book elaborate new branches of cybernetics, organization theory which studies as process, property and system.

• Bertalanffy, L. (1950). An Outline of General Systems theory.

An article states the fundamental of General Systems Theory. It interlinks systems amongst various sciences. Describes different aspects of systems have in common.

• V. Vasilakos, R. Kannan, E. Hossain and H. Kintis, “Special Issue on Game Theory,” in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 3, pp. 554-558, June 2010.

This paper discusses about the issues in game theory and the need to bring the state of art researches that states the theoretical models of game theory and the major issues that could be tackled by applying game theory to the different fields.

• D. W. North, “A Tutorial Introduction to Decision Theory,” in IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 3, pp. 200-210, Sept. 1968.

doi: 10.1109/TSSC.1968.300114

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4082149&isnumber=4082145

This paper provides insights on the foundation of decision theory. The main intent of this paper is to provide a basics on the topic on concepts of decision theory.

• A. M. Wildberger, “Complex adaptive systems: concepts and power industry applications,” in IEEE Control Systems Magazine, vol. 17, no. 6, pp. 77-88, Dec. 1997.

doi: 10.1109/37.642976

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=642976&isnumber=13983

This paper focuses on the understanding of complex systems that emerge from simple components but are adaptive agents. It can also be used to have ideas on how CAS can be useful for new business strategies for internal reorganization.

• Albus, J., Toward a

             Computational Theory of Mind. Albus – Journal of Mind Theory. Vol. 0 No. 1.

This paper is all about how science explains our brain and technology of intelligent systems. It also explains about how our brain can make decisions or make any judgement. Thus, to write about computational theory of mind this paper helped a lot to understand the concepts.

• Cristina Mele, J.P., Francesco Polese, A Brief Review of Systems Theories and Their Managerial

Applications. Institute for Operations Research and the Management Sciences, 2018.

This paper is useful for the people who are doing research on system theory. It discusses about the cybernetics, general system theory , sociology-biology, System theory application etc.

• Adams, K.M., Hester, P. T., & Bradley, A historical perspective of systems theory. Industrial and Systems Engineering Research Conference. 2013.

This paper is about the history of systems theory i.e. classical approaches. It tells about the various disciplines describing system theory with their perspective.

• A. Mahanti, N. Carlsson, A. Mahanti, M. Arlitt and C. Williamson, “A tale of the tails: Power-laws in internet measurements,” in IEEE Network, vol. 27, no. 1, pp. 59-64, January-February 2013.

Mahanti et al provide examples of internet measurements that suggest power-law behavior and its implications on improvements to internet systems.  These examples are used to provide evidence for isomorphic processes in information technology.

• M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” in IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, April 1997.

Dorigo and Gambardella develop an algorithm based on the pheromone trails of ants to generate solutions for the traveling salesman problem.  This algorithm is evidence for the usefulness of the study of isomorphic processes between biology and computer science.

• Mobile cellular subscriptions,” World Bank Group. [Online]. Available: https://data.worldbank.org/indicator/IT.CEL.SETS?end=2016&start=1985. [Accessed: 10-Sep-2018].

Graphs from the world bank database for statistics related to telecommunications.  Of interest is the graph of mobile cellular subscriptions, which suggest that cell phone adoption is following a logistic growth function.

• L. Muchnik, S. Pei, L. C. Parra, S. D. S. Reis, J. S. A. Jr, S. Havlin, and H. A. Makse, “Origins of power-law degree distribution in the heterogeneity of human activity in social networks,” Nature News, 07-May-2013. [Online]. Available: https://www.nature.com/articles/srep01783. [Accessed: 10-Sep-2018].

Muchnik et al. build a probability distribution model based on data on the activity of users of Wikipedia and a news-sharing          website news2.ru.  They find that the behaviors of these users follow a power-law distribution, providing further evidence for isomorphic process in IT.

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