transdisciplinary science methodology as a necessary

Orhan GuvenenTransdisciplinary Science Methodology as a Necessary Condition in Research and Education 69

Transdisciplinary ScienceMethodology as a NecessaryCondition in Research andEducationOrhan Guvenen,Bilkent University, Professor of Strategic Decision Systems and Econometrics,Ankara, Turkey, Email:[email protected]

Received 4 June 2016; Revised 4 October 2016; Accepted 5 October 2016

Copyright c©2016 Orhan Guvenen. This is an open access article distributed under the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited.

Available online 6 October 2016 at www.atlas-journal.org, doi: 10.22545/2016/00080

Traditional methods are not sufficient to dealwith highly complex questions of modernworld. Transdisciplinarity is mandatory to

understand and solve those problems, which existwithin the domain of multiple disciplines. Subsets’optimal does not always equal to the optimal ofthe general system set structure. Transdisciplinaryapproach is mainly focuses on a process of synthesisand aims to reach set optimal. The amount ofdata produced and distributed through moderncommunication channels is huge and remarkablepercentage of this data is not genuine. That leads toa phenomena which is called information distortion.To minimize information distortion and maximizeinformation security principles of hermeneuticsmust be embraced. A transdisciplinary approach ineducation is required to educate future generationsto deal with complex problems of the world. Thedominant system of the world is System 2, whichis driven by power and money. The dominance ofSystem 2 causes serious problems at world level

like conflicts, wars, human traffic, drug traffic anddegradation of natural resources use. To preventsuch problems and to solve the existing ones System1, which is based on science, technology, innovationand uses ethics as a constant, should be embraced asthe dominant world system.

Keywords: A-STEM-H, transdisciplinary domain,trans-sector, transdisciplinary curriculum.

1 Introduction

The scope of science and its structure is constantlychanging and evolving. As the science progressesover the time, it has to deal with more complicatedissues and manage to come up with minimum errormargin to scientific explanations and solutions. Dueto the iterative and evolutionary nature of science,in time dynamics, more complex ones emerge. Mostof the complex scientific issues exist in the domainof multi scientific disciplines.

Transdisciplinary Journal of Engineering & ScienceISSN: 1949-0569 online, Article ID 00080, 2016 TheATLAS

Vol. 7, pp. 69-78, 2016


Figure 1: General system set interaction and subset structure (Guvenen, 2000).

To deal with sophisticated questions with highdegree of complexity, requires the cooperation ofmultiple scientific disciplines. It needs to be tar-geted to the problem, analyse, interpret, convergeto solutions with an iterative transdisciplinary ap-proach which endogenize various disciplines.

The complex scientific issues that deals with mul-tiple disciplines used in general the following ap-proaches:

• Intradisciplinary Science Metholodgy

• Interdisciplinary Science Methodology

• Multidisciplinary Science Methodology

• Transdisciplinary Science Methodology

Intradisciplinary Science Methodology: Sciencemethodology which concentrates on research andeducation systems within the same scientific disci-pline.

Interdisciplinary Science Methodology: Re-searchers interact with the goal of transferring knowl-edge from one discipline to another. Allows re-searchers to inform each others work and compareindividual findings.

Multidisciplinary (Pluridisciplinary) ScienceMethodology: Researchers from a variety of dis-ciplines work together at some point during aproject, but have separate questions, separateconclusions, and disseminate in different journals.It is dominantly a synthesis approach.

Transdisciplinary Science Methodology: It can bedefined by following points:

• Being targeted to complex phenomena

• Working in an iterative approach

• Continuous cooperation of various sciences anddisciplines converging to the solution of deter-mined problem.

Figure 1 [1] shows the interactions between setstructure and subset structures. As it is well known,the sum of the subsets’ optimal is not equal to theset optimal. This theorem can be summarized withthe formula below:∑

subset′ optimal 6= Set Optimal

In the light of the information provided by the the-orem it is possible to estimate that both approaches,interdisciplinarity and multidisciplinarity (pluridisci-plinarity), leads to solutions which are under optimaland cannot use the full potential of the scientific dis-ciplines which are used in research and education.To converge to the optimal solution, the transdisci-plinary science methodology can be considered as anecessary condition.

In Arise 2 report by American Academy of Artsand Sciences there is an important observation whichis : “Efforts to take advantage of these opportuni-ties, however, have met significant barriers. Thecurrent organization of the research sector compli-cates communication and collaboration across disci-plines. Furthermore, fundamental advances are notbeing translated efficiently into new products andservices.” [2] This observation clearly states thatlack of proper communication and low level coopera-tion between different sciences and disciplines causesserious problems to converge to optimal solutions.Joseph E. Brenner describes transdisciplinarity asbeing supported by three major conceptual “pillars”which are complexity, levels of reality and logic ofthe included middle or third [3]. He asserts that


Vol. 7, pp. 69-78, 2016


the general methodology of the transdisciplinarityis based on those three conceptual pillars and theroots of those pillars lie within the domain of modernscience.

2 Transdisciplinary ScienceMethodology , InformationSystems, Big Data andInformation DistortionInteractions

As it has been previously mentioned above both in-terdisciplinary and pluridisciplinary methodologiesobtain general scientific data from each disciplineas the first step in research. They combine the ob-tained data, make a synthesis and come up witha final result. The limited interaction of these ap-proaches causes under optimal result; because thesum of subsets’ optima usually isn’t equal into beingset optimal. Interdisciplinary and multidisciplinary(pluridisciplinary) approach focus on the subset andpartial analysis which lead to quasi systematic andunder optimal end solutions in social sciences be-cause of not comprehending the properties of socialphenomena.

There are three main structural properties of socialphenomena. These are:

• Mathematical complexity

• Mathematical chaos

• Low predictability

To understand the true nature and essence ofsystems that social sciences deal with, it is importantto consider all these factors together.

It is important to develop an exterior point ofview to see the set structure and create an optimalsolution in the light of the factors mentioned above.It is not possible to solve a problem of a specificdiscipline by using only the data and methods ofthat scientific discipline. Input from various otherscientific disciplines and contribution of scientistswith different sets of skills are required to come upwith an optimal solution.

Transdisciplinary research includes the key com-ponents of interdisciplinarity, along with the incorpo-ration of external nonacademic knowledge, appliedto solve practical problems. Transdisciplinary re-search leads to a creation of new paradigms and

provides pathways to new frontiers. Atilla Ertasstates that transdisciplinary research includes keycomponents of interdisciplinarity with the support ofnon-academic knowledge which forms an applicationto solve practical problems; thus trandisciplinaryresearch changes paradigms and forms new frontiers[4].

The structures involved with science, society andespecially individual are known for being mathemat-ically complex, chaotic and low predictability. If weconsider these structures as functions of “n” vari-ables tends to the infinity. A change in one or fewvariables, can cause serious changes in end results.

One of the most important contemporary key con-cept in modern world is called big data. Big datais a term which is used to describe the exponen-tial growth and availability of data in both struc-tured and unstructured ways. Big data maybe asimportant to business and to the society, as muchas Internet has become [5].

Big data creates an extreme volume of data.100terabytes of data are uploaded daily to Facebook;Akamai analyses 75 million events a day to target on-line ads; Walmart handles 1 million customer transac-tions every single hour. 90% of all data ever createdwas generated in the past 2 years. ”90% of datagenerated is ‘unstructured’, coming in all shapes andforms- from geo-spatial data, to tweets which can beanalyzed for content and sentiment, to visual datasuch as photos and videos” [6].

In recent research by The Academy of Transdisci-plinary Learning & Advanced Studies, informationhas been defined as: “conjunction of the energeticprocesses involved in the transmission and recep-tionof meaning and that meaning, such that infor-mation cannot be separated from the underlyingphysical processes of its generation” [7]. The articleconcludes that information is something that lieswithin, between and beyond all disciplines, whichcarries strong similarities with Brenner’s transdisci-plinarity describtion mentioned in the earlier partsof this article.

As it can be seen from the information providedabove, big data opens a new era for social sciencesby providing a huge amount of cumulative data asnever seen before. However the increase in quantityof the information available to access does not au-tomatically guarantee the increase in quality andauthenticity of the information. While some of theseinformation are genuine, some of them might be


Vol. 7, pp. 69-78, 2016


Figure 2: The linkage between data and decision (Guvenen, 2014).

wrong or simply affected by a phenomena called“information distortion”.

It can be safely mentioned that the informationcan be distorted, manipulated, distracted or influ-enced in order to mislead the user of the informationfor a specific purpose. There are various examplesto these phenomena from all disciplines which all ofthem created very high social and economic alterna-tive costs which are:

• Distortion of probabilities and outcome infor-mation in the direction of preferred decisionalternative

• Information distortion can occur where the num-ber of distinct pieces of information on the net-work increases

“Hermeneutics” is a fundamental rule in sciencemethodology. The initial source, the initial signalsmay avoid in the time and space dynamics, highnegative impacts and alternative costs that mayoccur [8].

Figure 2 [9] represents the path data takes andnodes it passes while transforming into the decisionin the end. It clearly shows that distortion at anypart of the linkage will affect the end result, inthis case decision, in a negative manner. So it isreally vital to start with reliable and distortion freedata to come up with right decisions by using valueadded decision analysis and transdisciplinary sciencemethodology.

Mathematical complexity and chaos models canbe examined within the scope of non-lineer math-

ematic. Error margins and information distortionin complex and chaotic systems cause serious prob-lems for science and application areas. Due to thatreason, the use of specific analysis and evaluationmethods, which decreases the error margins andinformation distortion,increases the quality of theresearch and strength of the decision system. Figure3 [10] illustrates the importance of transdisciplinaryscience methodology in minimizing error marginsand information distortion.

“Information pollution” is an important challengeas environmental pollution, and it has to be consid-ered and resolved sensibly.

3 Anticipative Analysis andTransdisciplinary ScienceMethodology

Anticipative analysis is endogeneous to trandisci-plinary science methodology, especially in highlycomplex structures of social sciences would convergethe decision systems to optimality and minimizealternative costs.

Anticipative analysis examines the solution ofproblem “P1” in time interval “t1” [11]:

• In analysis and research if the decision makerdecides to solve the problem “P1” in time in-terval “t1” and use the estimated and requiredsolution approach, analysis and research, theprobability is relatively high that problem canbe solved as an example with “1 unit cost”.


Vol. 7, pp. 69-78, 2016


Figure 3: Minimizing information distortion and transdisciplinary science methodology (Guvenen, 2014).

Figure 4: Importance of strategic anticipation in research (Guvenen, 2015).

• If the solution in time “t1” is postponed fora definite amount of time, like five years, thestructure of the problem might change. Thismight increase the alternative costs and the costof solving the problem “P1” , and it may be 8,10 or more units.

• If the problem p1 is ignored in time muchlonger, the important changes in system dy-namics, structures and problem environmentmight turn the problem into being unsolvable

due to public, private decisions, environmental,sustainability, e.g. constraints.

Obtaining data, making observations and antic-ipating the future trends carries significant impor-tance in scientific progress. Figure 4 [12] visualizesthe strict relation between science methodologiesand strategic anticipation. As it can be recognizedfrom the figure the particular scientific approach hasserious impact on the end result. Choosing an sub-optimal scientific approach can end up with serious


Vol. 7, pp. 69-78, 2016


Figure 5: Complex structure of social sciences and anticipative analysis (Guvenen, 2013).

alternative costs in researches.

Figure 5 [13] represents the complex, intricatedand interrelated nature of social science systems.Due to complex nature of these systems, anticipativeanalysis should be made by using transdisciplinarymethods to converge to optimal result.

The collaborative and cooperative nature andproblem targeted approach of Transdisciplinary sci-ence methodology can minimize the common prob-lems and shortcomings of both pluridisciplinary andinterdisciplinary science methodologies.

Transdisciplinary implies an integration-drivenemergence of new disciplines, not just ad hoc col-laborations. The transdisciplinary nature of currentscientific and societal challenges–and the powerfulnew approaches enabled by the combination of tradi-tionally separate disciplines–can be fully addressedonly by a rethinking of current academic and govern-ment funding structures, as well as the traditionalrelationships among academia, the private sector,and government.

4 Transdisciplinary sciencemethodology in education

One of the most important areas which transdisci-plinary approach should be applied is the educationsystem. The structural changes in science and soci-ety caused important changes in education system

too. In 13th century when the first universities hasbeen founded, the number of disciplines were only7, however now there is more than 8000 scientificdisciplines [14]. This paradigm shift shows why itis impossible to educate the future generations withthe education system of the past.

The existing education structure generally is basedon “department” system. “Department” dominantlytransmits an education of subset and partial analy-sis. It does not provide the methodology of linkingsubsets within an iterative set approach of trans-disciplinarity. Figure 6 [15] illustrates the relationbetween department system and complete system ineducation.

The department system neglects to dominantly theimpact of science and knowledge elaborated by otherdisciplines which should be considered as complimen-tary to a given department teaching in a continousiterative approach. It tends to create an impact inthe medium-long term; mechanistic, short-termistapproaches in the society. The alternative costs ofthis subset and partial analysis approach are dom-inantly observed in the 20th century. 21st centuryscience, research, analysis requires transdisciplinarymethodology in order to deal with high complexi-ties and provide feasible solutions. Students’ needs,characteristics, interests and personal learning pro-cesses occupy the core of transdisciplinary educa-tional model.


Vol. 7, pp. 69-78, 2016


Figure 6: Transdisciplinary science methodology in education (Guvenen, 2013).

It is important that the transdisciplinary skillsto be taught to students during education includeknowledge and skills required to identify, frame andaddress important scientific and practical problemsthat cut across disciplinary boundaries. Such prob-lems are complex and they require such skills as[16]:

1. Integration of problem framing and problemsolving

2. Communication and collaboration among peo-ple from different disciplines and educationallevels

3. Intelligent use of technologies and resources thatsupport collective knowledge construction andextend human problem-solving capability.

Educational institutes are evolving to answer thedemand for education of transdisciplinary skills.Actually universities are going through significantchanges which they evolve from science-based , gov-ernment funded institutions into international know-how hubs which is called third generation universities[17].

The concept of Transdisciplinarity and its prac-tices which endogenize not only research, educationbut also daily life, decisions at the individual, corpo-rate, institutional, nation state, international spheremust be a part of teaching, learning methodologyfor the earliest ages in school programmes.

Figure 7 [18] illustrates the basic traits of thirdgeneration universities. One of those traits and prob-ably one of the most important, is transdisciplinaryresearch. The fundemental research core of thirdgeneration universities are based upon the principlesof transdisciplinary science methodology to ensure acompetitive and modern university education.

Third generation universities through fundamen-tal research, education, business and corporationscontinuous cooperation, complementary endogenizetransdisciplinarity to universities. Their contributionincrease the value added to research and education.

5 Transdisciplinary sciencemethodology and ethics

Figure 8 [19] above represents the relationship be-tween the System 1 (S1) and System 2 (S2). S1 canbe described as the structure of world dynamics tar-geting “humanity optimality”. S1 is the normativeapproach. System 2 (S2) represents the structure ofworld dynamics in 2016.

The dominance of S2 causes crisis and serioussocio-economic problems. 2008 economic crisis is oneof them. In the context of the 2008 world crisis, weobserve that the world GDP was $60 trillion at thattime. However, the amount of financial operationsin investment banking and markets were over $600trillion. Concerning this huge amount, we observethat there was not, and today there is not any legal,economic and regulatory framework concerning theseoperations and markets. It can be considered thatit is appropriate to call it : a “crisis of ethics”.

As long as we live in a world which S2 is dominant,such crises, world level conflicts, wars, human traffic,drug traffic, degradation of natural resources use,nature, environmental problems are unavoidable. Weneed to transform system into a brand new one whichrelies on human optimal; in other words we shouldfocus on the values which S1 built on. Science,technology and transdisciplinarity are among themost important of those values. Ethics is a necessarycondition for the set optimal [20, 21].


Vol. 7, pp. 69-78, 2016


Figure 7: Third generation universities (Aarts, 2014).

Figure 8: World dynamics and some comments on socio-economic systems (Guvenen, 2013).

SetOptimal = α0 + α1x1 + α2x2 + α3x3...+ ε

“α0” is necessary condition, which represents

ethics whether at individual, corporate, institutional,nation state, transnational corporations, interna-tional organizations and international sphere levels.


Vol. 7, pp. 69-78, 2016


6 Conclusions

Any search for a system optimal requires that ethicsmust remain constant in the dynamics of time andspace at the individuals, institutions, corporations,nation states and international level. In preventinginformation distortion; the starting point, the ini-tiator, the igniter constant should be ethics for theregulators and for the information system securitypractice.

This approach should include the other explana-tory variables in order to understand and minimizethe error margins of the complex structures and de-cision systems which endogenize transdisciplinaryscience methodology.

References

[1] Guvenen, O. (2000). The interaction between econo-metrics, information systems and statistical infras-tructure: anticipation and comparative analysis ina decisional structure. Journal of the Turkish Statis-tical Association 3(1-2), 22-43.(Non-published paperpresented at the University of Europe Conference,Aix-en-Provence, 1971).

[2] American Academy of Arts and Sciences. (2013).ARISE 2: advancing research in science and en-gineering : unleashing America’s research & in-novation enterprise. Cambridge, Mass: AmericanAcademy of Arts and Sciences.

[3] Brenner, J. (2014). Sytems and information: trans-disciplinary study. Transdisciplinary Theory & Prac-tice. Nicolescu, B., Ertas, A. (Ed.) (pp. 4, 17- 19) .ATLAS Publishing.

[4] Ertas, A. (2013). Integrating transdisciplinarity inundergraduate education. Transdisciplinary Theory& Practice. Nicolescu, B., Ertas, A. (Ed.) (p. 191).ATLAS Publishing.

[5] SAS. (2014). What is big data? Business Analyticsand Business Intelligence Software. SAS, Web. 05Nov. 2014.

[6] McNulty, E. (2014). Understanding Big Data: theseven V’s.

[7] Brenner, J. (2014). Sytems and information: trans-disciplinary study. Transdisciplinary Theory & Prac-tice, Nicolescu. B., Ertas, A. (Ed.) (pp. 4, 17- 19) .ATLAS Publishing.

[8] Gadamer, H-G. (1976). Philosophical Hermeneutics.Berkeley: University of California Press

[9] Guvenen, O. (2014). Knowledge, data, informationerror margins and information distortion interac-tions. Conference on Statistics, Science and PublicPolicy. Queen’s University, April 22, 2014, Kingston.

[10] Guvenen, O. (2014). Knowledge, data, informationerror margins and information distortion interac-tions. Conference on Statistics, Science and PublicPolicy. Queen’s University, April 22, 2014, Kingston.

[11] Guvenen, O. (2013). Mathematical complexity,chaos, and the impact of information distortionon decision systems in social sciences. Impact ofScience and Technology on Society and EconomyConference. World Academy of Arts and Science(WAAS), 5 March, 2013, Trieste, Italy.

[13] Guvenen, O. (2015). Some comments on trans-disciplinary science methodology. Library LunchtimeLecture, Bilkent University, April 2, 2015

[12] Guvenen, O. (2013). Anticipative analysis, informa-tion systems and social responsibility driven decisionanalysis. Presented at the Institute of Operations Re-search and the Management Sciences Annual Meet-ing. 2013, INFORMS, 4 October 2013, Minneapolis.

[13] Nicolescu, B. (2014). The need for transdisciplinarityinhigher education in a globalized world Transdisci-plinary Theory & Practice. Nicolescu, B., Ertas, A.(Ed.), p. 17, ATLAS Publishing.

[14] Guvenen, O. (2013). Mathematical complexity,chaos, and the impact of information distortionon decision systems in social sciences. Impact ofScience and Technology on Society and EconomyConference. World Academy of Arts and Science(WAAS), 5 March, 2013, Trieste, Italy.

[15] Simon, H. A. (1996). The Sciences of the Artificial.3rd ed., The MIT Press, Cambridge, MA.

[16] Wissema, J. G. (2009). Towards the Third Gen-eration University: Managing The University inTransition. Edward Elgar Publishing.

[17] Aarts, E. (2014, September 29). Towards a third gen-eration university. retrieved March/April, 2016, fromhttps://www.tue.nl/uploads/media/140929−Emile−Aarts−towards−3rd−generation−universities.pdf

[18] Guvenen, O., (2008), “Economic Prosperity, Inter-action with Science, Knowledge and Value Systems”in Statistics, Science and Public Policy XII, ed. A.M.Herzberg, Kingston: Queens University, p. 49, ISBN:978-1-55339-152-4

[19] Guvenen, O. (1971). Interdisciplinarity and transdis-ciplinarity, some comments on science methodology.Non-published paper presented at the University ofEurope Conference, Aix-en-Provence, France


Vol. 7, pp. 69-78, 2016


[20] Guvenen, O. (1998). Theorem presented at the“Club of Rome Annual Conference”, Quito University,Equador

About the Author

Professor Orhan Guvenen is an expert in the fields ofStrategic Decision Systems, Information Systems, Econo-metrics and International Economy. He holds a B.Sc. inEconometrics from stanbul niversitesi, and his M.Sc., inEconometrics, from the Universite Paris-Sorbonne (ParisIV), France. He was awarded his Ph.D. in Econometricsand International Economy, also from La Sorbonne, in1975. From 1979-1988, Prof. Guvenen was ResearchDirector and Professor of Applied Econometrics at theUniversite Paris, during which time he also worked withthe Organisation for Economic Co-operation and Devel-opment (OECD), Paris.

Prof. Guvenen joined Bilkent University in 1988, as

Professor of Strategic Decision Systems, Econometrics

and International Economics, and as founding director of

the Institute of World Systems, Economies and Strategic

Research (DSEE). He has been Chair of the Depart-

ment of Accounting Information Systems since August

2000, and is also a member of the executive board of the

UNAM National Nanotechnology Research Center, as

well as chairman of the Advisory Committee on Strategy,

Economy and Industry at Bilkent. From 1997 – 2009,

he worked closely with the Prime Ministry of Turkey

and held various administrative roles for UNESCO. He

is currently the president of the Applied Econometrics

Association. Prof. Guvenen has published numerous

books and papers internationally and in Turkey, and

holds many awards and honors. As well as being a native

Turkish speaker, he also speaks fluent English, French

and Italian. He is Invited Professor of the University of

Paris Pantheon - Sorbonne, Fellow of World Academy

of Art and Science,President of “Applied Econometrics

Association”.


Vol. 7, pp. 69-78, 2016

transdisciplinary science methodology as a necessary

Documents