Systems Thinking - Science topic

While undergraduate engineering programs incorporate courses that address systems thinking, the depth and practical application vary considerably. Some programs offer courses dedicated to systems thinking, while others integrate it into different subjects. The effectiveness of teaching systems thinking depends on the experience of the teachers, the methodology used and the relevance of the examples used.

In general, there is a need to strengthen the teaching of systems thinking in engineering degrees so that future professionals can approach complex problems comprehensively and consider the interrelationships between the different components of a system.

We must not limit our boundary of what we regard systems thinking to be, based on one method, i.e. SD. There are more than 10 relevant methods or approaches on ST, and the best definition of this I've seen relates to what is expected of ST practitioners, from an employer perspective. This is built into the Systems Thinking Practitioner L7 standard in the UK, covering not just skills, but also knowledge and behaviours, and anyone can access a copy at: https://www.instituteforapprenticeships.org/apprenticeship-standards/systems-thinking-practitioner/

Hello Mr. Weaver.

I think it is a very interesting theme.

thank you,

Olga Gold

systems thinking enhances the quality and effectiveness of strategic foresight by providing a more comprehensive and interconnected perspective on complex issues. It helps strategists better understand the dynamics of systems, anticipate potential outcomes, and develop strategies that are more resilient, adaptive, and ethical.

In systems thinking, everything is considered ideal and solutions may be linear and straightforward, but many parameters are considered in human thinking.

Of course, this is my opinion, maybe the definition is something else

The engineering design process can have a positive impact on system thinking skills. Here are a few ways in which the engineering design process can affect system thinking skills:

1. Holistic Perspective: The engineering design process encourages a holistic perspective, where engineers consider the various components and interactions within a system. This systems thinking approach helps engineers understand the interconnectedness of different elements and how they contribute to the overall functionality of the system.

2. Problem Analysis: During the engineering design process, engineers analyze problems and identify the root causes. This analysis requires them to consider the system as a whole, examining how different components and factors interact and influence each other. This promotes critical thinking and helps develop the ability to identify and understand complex systems.

3. Iterative Approach: The engineering design process often involves multiple iterations and feedback loops. Engineers continuously evaluate and refine their designs based on feedback and testing. This iterative approach fosters the ability to analyze and understand the feedback from a system, adapt designs accordingly, and consider how changes in one part of the system may impact other parts.

4. Interdisciplinary Collaboration: Engineering design often involves collaboration with professionals from various disciplines. This collaboration encourages interdisciplinary thinking and the consideration of multiple perspectives. Engineers must understand how different disciplines and subsystems interact and work together to achieve the desired outcomes.

5. Consideration of Constraints: The engineering design process requires engineers to consider various constraints, such as budget, time, materials, and environmental impact. This consideration of constraints necessitates a systems thinking approach, as engineers need to understand how different factors and limitations affect the overall system and its performance.

Overall, the engineering design process promotes system thinking skills by encouraging engineers to analyze problems holistically, consider feedback and iterations, collaborate with interdisciplinary teams, and understand the interdependencies within a system. This mindset and skill set are valuable not only in engineering but also in various fields where understanding and analyzing complex systems is crucial.

I fundamentally concur with Ajit Singh's answer. One of the most important aspects of systems thinking is the elicitation/identification of the needs of stakeholders with an interest in the system. Limiting your thinking to the present context will result in a system (or system understanding) that addresses only the present. As the "context system" in which the system of interest lives and functions changes and adapts over time, the system design or understanding will lose its relevance because the future is becoming the present context for the system. The more accurate and robust your understanding of the future (gained through your up-front futures thinking) the better your understanding/design will retain its relevance.

Lots of definitions in the literature:

SEBok https://sebokwiki.org/wiki/Systems_Thinking

UK Government

NHS Education for Scotland https://learn.nes.nhs.scot/6027

...or this one from Arnold & Wade (2015):

Take your pick!

Basically each process as mentioned in your question can become a 'sub system', and thereafter connected to each other based on dependence levels. Eventually it will lead to a bigger comprehensive system of knowledge management. Be creative when designing the system.

Dear Bangaly Cisse,

I would suggest that you explore the Use of Blockchain to increase the Security of some Types of Digital Twins (DTs) - such as Human Health DTs (HHDTs).

General Information about Digital Twins can be found at the Address:

My small List of Literature is intended to give you the Basics for using Blockchain in System Solutions with DTs.

Best regards

Anatol Badach

Haya R. Hasan, Khaled Salah, Raja Jayaraman, Mohammed Omar, et al: A Blockchain-Based Approach for the Creation of Digital Twins; IEEE Access, Vol. 8, Feb 2020, DOI: 10.1109/ACCESS.2020.2974810

Sabah Suhail, Rasheed Hussain, Raja Jurdak, Choong Seon Hong: Trustworthy Digital Twins in the Industrial Internet of Things with Blockchain; arXiv:2010.12168v1, Oct 2020; DOI:10.1109/MIC.2021.3059320

Haya R. Hasan, Khaled Salah, Raja Jayaraman, Mohammed A Omar, Ibrar Yaqoob, Sasa Pesic, Todd Taylor, Dragan Boscovic: A Blockchain-Based Approach for the Creation of Digital Twins; IEEE Access, Feb 2020, DOI: 10.1109/ACCESS.2020.2974810

Ibrar Yaqoob, Khaled Salah, Mueen Uddin, Raja Jayaraman, Mohammed Oma, Muhammad Imran: Blockchain for Digital Twins: Recent Advances and Future Research Challenges; IEEE Network, Vol. 34, Issue 5, Sep/Oct 2020; DOI: 10.1109/MNET.001.1900661

For the past 150 years, economics has been treated as a social science in which economies are modeled as a circular flow of income between producers and consumers. In this "perpetual motion" of interactions between firms that produce and households that consume, little or no accounting is given of the flow of energy and materials from the environment and back again. In the standard economic model, energy and matter are completely recycled in these transactions, and economic activity is seemingly exempt from the Second Law of Thermodynamics. As we enter the second half of the age of oil, and as energy supplies and the environmental impacts of energy production and consumption become major issues on the world stage, this exemption appears illusory at best. In Energy and the Wealth of Nations, concepts such as energy return on investment (EROI) provide powerful insights into the real balance sheets that drive our "petroleum economy." Hall and Klitgaard explore the relation between energy and the wealth explosion of the 20th century, the failure of markets to recognize or efficiently allocate diminishing resources, the economic consequences of peak oil, the EROI for finding and exploiting new oil fields, and whether alternative energy technologies such as wind and solar power meet the minimum EROI requirements needed to run our society as we know it. This book is an essential read for all scientists and economists who have recognized the urgent need for a more scientific, unified approach to economics in an energy-constrained world, and serves as an ideal teaching text for the growing number of courses, such as the authors' own, on the role of energy in society.

(Citation from link).

thank Christopher Kelly glad to have you with us!

Oleksandr Tarasov indeed, but I've heard arguments (like some have argued here) that we shouldn't teach systems thinking in the first year of engineering education, because the students don't have the required knowledge and skills to make sense of it or apply it.

Dear Daniel Guzzo,

I suggest you see links on the topic.

Best regards

IR is focused on showing the connectivity of strategic objectives, risk and performance to demonstrate how organisations create value. This means that organisations need to understand and report on all areas of performance and not just focus on short-term financial results.

Dear Mr. Alzahawi:

Could you answer in english?

Thanks

Gustavo Concari

It is recommended to follow one quite useful part of complex systems modelling methodology: agent based modelling. There might be different types of agents, some in a few copies like politicians, other in larger numbers like society cliques. There might be defined engagement rules among different types of agents.

Dirk Helping is definitely having research on societal interactions. The seminal book on agent based modelling is by Illachinski: Artificial War. There is explained how to program agents to achieve their desired behavior.

Entropy measures can be used to classify the mode of behavior of the system. Each system undergoing a phase transition express it in changes of entropy even when globally there is nothing substantial to observe!

Rea Lavi

I don’t believe creativity in systems thinking is limited to new users. I say this because I think people aren’t always aware of all the possibilities inherent in a system, its ramifications, potential, application to other domains, generalizeability, nuances, or whatnot. In other words, people who employ a system, even ones that they were instrumental in devising, can be shortsighted and hidebound about it.

I think of the methodological approach to philosophy dubbed Ordinary Language Philosophy and its central idea of “meaning as use”. A lot of mileage was gotten from notions of what one would or wouldn’t say under certain circumstances and from what would be considered “deviant utterances”. It took creative minds to realize that a distinction between “truth conditions” and “assertion conditions” was consistent with the terms of reference of the methodological framework. Noncreative minds had adhered to the letter rather than the spirit of the methodology.

Analysis, synthesis, systems thinking and the scientific method: Rediscovering the importance of open systems

DOI:10.1002/sres.816

Yes, under the integrated water management approach advocated in Dublin, it talks about the systems approach.

Meta-analysis is done when there is a quantitative data in the papers you collect to find out and effect size.A systematic literature review but can be done for your type of research.Pls refer to the video https://www.youtube.com/channel/UCj38Topqj8yC3SdrV6HypIg

Hello,

In fact, sustainability is not a single research topic, but a multi-disciplinary integration topic and the general research is to study sustainability in a certain field. The specific categories include energy, technology, environment, management, policy, agriculture, and so on. Please check the following journals:

Nature Sustainability:

Hope it may help you.

Best Regards,

Ning Zhang

Uno de mis ámbitos de trabajo es el pensamiento estratégico, estudio de casos en ecuador. Puedo estar interesado.

The creation of different scenarios and the interactions. Look at some papers of mine hoping that these could help you

This is an interesting question that has the potential to elicit a variety of responses depending on the disciplinary context of your inquiry. For example, an economist will likely have a different perspective on systems thinking than that of an evolutionary biologist, and yet the systems thinking model is useful for both academicians, and many others.

Generally speaking, the broad transdisciplinary understanding of systems thinking can be found in the work of the late Donella Meadows, a Harvard biophysicist who went on to apply systems thinking to environmental science and conservation at MIT. Her work is very accessible for the advanced undergraduate, I believe, from a variety of disciplines. The Limits to Growth (1972) and Thinking in Systems: A Primer (2008) are still very relevant.

Soft systems thinking would include qualitative visual modeling approaches which can provide information about the dynamics of a system. Soft analyses can be drawn (at a high level) with pen and paper, even for real-world systems such as a municipal waste system, a natural ecosystem, a population of arctic foxes, or the system of dairy milk production.

The soft systems thinking approach is a schematic representation, not unlike a flow chart, concerned with the dynamics of finite resources and the stocks and flows of those resources through the different nodes of the system. By drawing a schematic, one can analyze causal relationships that can create quite complex behavior, such as feedback loops, and vicious or virtuous cycles. One of the commonalities that all complex systems share its this: the system as a whole is more than the sum of its parts.

Hard systems thinking includes mathematical models that either approximate or describe the dynamical nature of a system. A good example of the difference between the soft and the hard approach can be found in meteorological models, such as the ones used by NOAA to track (and predict) severe weather systems, such as hurricanes. The weather map that you might see on the news is a (perhaps somewhat simplistic) yet accurate schematic of the weather system that produces a hurricane, and yet NOAA scientists are using more sophisticated mathematical tools to understand such a system as it evolves, and make useful predictions about the system's behavior.

The mathematical approach includes both dynamical (numerical) and statistical models. One dynamical model is the deceptively simple-looking logistic difference equation (Lorentz and Feller), which is actually quite useful across disciplines for understanding real-world dynamical systems that produce stunningly complex behavior, including chaos. [There is a seminal paper that you can read if you are interested in chaos: "Period Three Implies Chaos" (Yorke, et al).]

Hurricanes, as I mentioned above, are a great example of a system that can be modeled with relatively simple system of ordinary differential equations (Lorentz/Fetter), and yet such a system is still notoriously difficult to make predictions about, precisely. A meteorologist will rely on both soft and hard systems approaches to model and understand the path and intensity of a hurricane, and even with the enormous computing power available today, the extreme sensitivity to initial conditions makes it nearly impossible to make exact predictions after relatively few iterations.

Please feel free to reach out if you need suggestions for further reading. Complex systems are all around us in the real world, from social networks; to workflows in manufacturing; to population dynamics and beyond, and there is still much to be discovered.

To understand the similarities and differences between Digital Twin and CPPS, I would suggest the following description:

Cyber-Physical Systems are an integration of digital data and cyber methods with physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops, where physical processes affect computations and vice versa. The Digital Twin is a virtual representation of a physical asset in a Cyber-Physical Production System (CPPS), capable of mirroring its static and dynamic characteristics. It contains and maps various models of a physical asset, of which some are executable,called simulation models.But not all models are executable, therefore the Digital Twinis more than just a simulation of a physical asset. Within this context, an asset can be an entity that already exists in the real world or can be a representation of a future entity that will be constructed.

Dear Colleagues and Friends from RG,

Less technologically developed countries build their economic development, among others, often by exploiting natural resources, exporting natural resources and low-processed products. This is not the best strategy for economic development, because it can quickly lead to the exploitation of natural resources of animate nature (e.g. deforestation of trees in forests) and resources of inanimate nature (extraction of energy and industrial raw materials contained in the earth's crust). Developing countries should gradually but as soon as possible limit the exploitation of natural resources of animate and inanimate nature and should remodel production processes by adding advanced technology and innovation to them. They should produce for export highly processed products with a high scale of added value by using technology as a key manufacturing factor. In addition, the reduction of energy extraction should be carried out with the simultaneous development of energy based on renewable energy sources, successively enriched with ecological innovations and technologies Industry 4.0.

In addition, the protection of biodiversity of natural biological ecosystems is one of the main challenges of sustainable ecological development to be implemented in the 21st century. However, the problem is the implementation of the principles of sustainable pro-ecological development into commercially implemented business processes. It is not easy to combine the green economy philosophy with the foundation of commercially operating business processes, i.e. classic economics. It is necessary for business communities to cooperate with other types of organizations, including pro-ecological social institutions and the world of science.

I believe that in the 21st century, in the 21st century, the policy of protecting nature and biodiversity of natural ecosystems should be improved due to the progressing global warming process and the increasing pollution of the environment. In connection with the reduction of green areas and forests, rehabilitation and greening of areas degraded by industrial development should be developed. Because the global warming process is accelerating, climate changes unfavorable for nature and biodiversity are deepening, so the issue of protecting natural resources and nature is a global problem. Therefore, the improvement of nature protection policy and biodiversity of natural ecosystems should also take place in the field of international cooperation.

Therefore, in developing countries, the goal of biodiversity protection should be to implement the principles of sustainable pro-ecological development in economic processes in the following years to a universal, aggregate, national and international level. The implementation of the principles of sustainable pro-ecological development should be carried out in accordance with the green economy philosophy and pro-ecological reforms should be financed from various sources, primarily from green finance and public finances of the state, and also the implementation of ecological innovations should finance enterprises whose production has a negative impact on the environment.

I wrote more about this in the comments, questions and answers on my Research Gate profile.

Do you agree with me on the above matter?

What is your opinion on this topic?

Please reply.

I invite you to discussion and scientific cooperation.

Thank you very much.

Best wishes.

Dariusz Prokopowicz

The KPIOnto - ontology serves as a schema to represent KPIs as a knowledge graph. The format is RDF a W3C standard coming from the Semantic Web, it helps when you want to link and integrate different data sources with the KPIs and run some graph analytics on top. We have used it already in some projects.

Integrated Management

Available at Emerald Press, AMAZON and other on-line platforms including Amazon Kindle, Google eBooks, Proquest, DawsonEra, Ebooks.com, MyiLibrary, EBSCOHost

Chapter 1 The Integration Opportunity

We Need Decision Makers with a Vision for a Sustainable Future

Why Now

A Dynamic Performance Frontier

Integration Operationalized

Trends – Evidence of the Inevitable

Chapter 2 Critical Dimensions of Integration – Enablers

Complex Problems

Systems Thinking

Value Creation

Change Management

Integrated Reporting

Chapter 3 A Customized Approach for Any Enterprise

Take an Action Learning Approach

Collaborative Action

Facilitating the Strategic Planning Process

Strategic Sustainable Development (SSD)

Enabling Integration Opportunities – ABCD Planning Process

Informed Decision Making

Integration and Change Management

Chapter 4 Integration Across Disciplines

Value Multiple Perspectives

Overcoming Obstacles - Functional Integration

Accounting,

Finance,

Human Resources,

IS/IT,

Marketing,

Operations,

Public Relations

Enabling Integration Opportunities

Get the System in the Room – The Role of Boundary Spanning Individuals

Chapter 5 Value Creation for Stakeholders and Shareholders

More on Materiality

Shareholder Value

Revenue Growth

Operating Margins

Asset Efficiency

Expectations

Making the Business Case

Chapter 6 Design Thinking – Life Cycle Assessment

Invent – Test – Bring to Life

What’s Your Net Zero Strategy?

Life Cycle Assessment – A Tool Supporting Design and Goals of Zero

Problem Based Learning Application

Why & How LCA/Design Thinking Enable Integration - Outcomes

Your Ecological Footprint

Chapter 7 Enterprise Systems – Operational & Strategic Assessment

Management Systems

Drivers, Barriers and Enablers

Strategic Planning: Integration Tools and Assessment Frameworks

SWOT, PESTEL, GE-McKinsey 9 Cell Matrix, Boston Consulting Group Growth Share Matrix, Ansoff Matrix, Porter’s 5 Forces, Product

Adoption Curve

Standards

4 Cs – Company, Customers, Competition, and Collaborators

Chapter 8 The Changing Performance Frontier – Evolution & Trends

Changing Performance Frontier – Materiality Map

Natural capitalism

Cradle to Cradle

Industrial ecology

Environmental Product Declarations (EPDs) and Living Products

High performance buildings

USGBC LEED, Energy Star, EUI, IFLI, Passive Haus, Well Standard, Green Globes

Chapter 9 Crossing the Chasm – Evidence & Opportunity

How Firms Integrate: Crossing the Chasm toward Sustainability

Social Cost of Carbon

CDP and GHG Protocol

GRI

Integrated Reporting, IIRC <IR>

Impact Investing

Benefits Corporations

Chapter 10 Propositions – Integration and Innovation

Integration and Innovation – Pushing the Performance Frontier

Propositions (1-4)

1-We rely on an unbalanced approach to financial analysis and reporting

2-We can account for environmental and social value

3-An Integrated Bottom Line offers a more complete picture of enterprise value

4- We can integrate environmental and social performance measurement into decision making

5- We should stress test investments with a new integrated approach

IBL, IntFV, IntRR, ROInt,

Tools to Help Assess and Prioritize

Multi-criterion Decision Analysis (MCDA)

Modeling for an Uncertain Future @Risk

Section V

Chapter 11 The Strategic Integrated Enterprises We Have Been Waiting For

Strategic Integrated Systems

Impacts of Corporations – Change Agents

High Level Measurement and Alignment Options

Turn Options into Priorities and Actions

UN Sustainable Development Goals

Chapter 12 The Future – What Could Be (Beyond Sustainability)?

A No Compromises Approach

Revisiting the Performance Frontier

Challenges

Innovative Solutions

Strategies, Functions, Systems, Value Chains, and Cities Offer Innovative Solutions

A Call to Integrated Management Action

Your Vision and Action Plan

Optimism

Integrated Management Resource Guide

Integrated Management Strategy Statement Appendices (actions you can take)

· Appendix 1 Examples of economic topics to review

· Appendix 2 Examples of social topics to review

· Appendix 3 Examples of societal topics to review

· Appendix 4 Examples of environmental topics to review

· Appendix 5 Examples of Sustainability Standards/Initiatives

Available at Emerald Press, AMAZON and other on-line platforms including Amazon Kindle, Google eBooks, Proquest, DawsonEra, Ebooks.com, MyiLibrary, EBSCOHost

There can be no doubt that systems thinking complements reductionism and that the world and our lives would be poorer without it. But, try as one might, systems thinking is not the be-all and end-all of approaches to problem solving because it:

may be of broader, tangential interest.

It is very important that, within an organisation, there is a Safety Management System which operates from top to bottom. At senior management meetings, there always needs to be a 'Safety' item on the agenda.

The concept of logging 'near misses' and 'first aid' is crucial to obtaining a comprehensive asessment of risk.

The next stage is to relate this data to varying working environments and also to the working hours of those employed. This can be organised into a computer program covering 24 hour days [where necessary] and seven days per week.

This will highlight any peaks in incidents.

I have used the above to monitor over 100,000 employees.

It highlighted that key times of risk occurred:

after 5 hours work without a break,

working more than 13 hours in a shift,

between the hours of 2am and 5am,

those staff returning to work after leave or illness.

Another area of risk to the company is where supervisory staff are required to monitor information continually [e.g. in control room in atomic reactors]. The average 'span-on-concentration' declines dramatically after 2 hours. The answer is to introduce short shifts with rest breaks.

I hope this is useful.

David

Good Day and good question, Karen.

Although Senge places purposes and reasonings within contexts of where humanity is coming from, where it is heading, and where an undertaking may be understood “as a vehicle for bringing learning and change into society” and as a source for providing “a single integrating set of [meaningful and potentially transformational] ideas” (Senge, 2004, p,321), there is still a remarkable neglect to tap into the synergies between organizational and personal learning.

As exemplified in the case of an ongoing Design Science Research (DSR) project aiming for a novel personal and generative Knowledge Management (KM) system (see latest references currently in press below), current wanting KM practices are a case in point: Aiming to explicate individuals' tacit knowledge for merely organizational utility, they are often failing to enthuse a skeptical workforce. To re-frame this cultural and technological paradigm, the DSR project alluded to aims to decentralize KM and to strengthen the autonomous role of an individual (and his/her personal learning environment) as contributor to and beneficiary of organizational and societal performances. Appealing to knowledge workers' self-interest in this way is expected to effectively foster a fruitful co-evolution between novel individualized and traditional institutionalized KM Systems.

https://www.researchgate.net/publication/334672236

Such integration is often complex and needs to consider a nested system of multiple scales and strategies. A Multiscale Ecosystem Framework can be used to achieve such a deliberate design. Here is a link to my article:

Perhaps could be useful to read the following paper: Scientific systems in Latin America: performance, networks, and collaborations with industry https://link.springer.com/article/10.1007/s10961-017-9631-7

I believe that the concept of distributed economies (DE) is closely linked to some of the aspects of Appropriate Technology (AT) popularised by economist E.F. Schumacher in his 1973 book "Small is Beautiful". Ian Smillie (2008, p. 91) defines AT as follows:

• It meets the needs of the majority, not a small minority, of a community;

• It employs natural resources, capital and labour in proportion to their long-term availability;

• It is ownable, controllable, operable and maintainable within the community it serves;

• It enhances the skills and dignity of those employed by it;

• It is non-violent both to the environment and to the people;

• It is socially, economically and environmentally sustainable.

Although AT lost a fair bit of academic steam as it became equated to a 'hippie' approach to Development, it nonetheless provides a very localised and situated perspective of DE and S.PSS prior to these terms becoming mainstream. In their expansive exploration of AT, Hazeltine and Bull, note that the “usefulness or value of a technology must be consolidated by the social, cultural, economic and political milieu in which it is to be used” (2003, p. 4). Coming from and working in the Global South, such a philosophical approach to design is really important for appropriate outcomes.

There is a significant literature on the use of system dynamics modeling in analyzing complex systems and testing policies. John Sterman argues that it is only through modeling, validation, and calibration that one can comment on complex systems, whereas, Peter Senge believes that systems thinking, as far as coming up with causal loop diagrams, can actually improve understanding and insight for complex systems. I think it depends on your way of viewing systems thinking. I believe Systems Thinking is an unconscious mental paradigm, a way of looking at the world that fundamentally changes our understanding of complex systems. In this context, Systems thinking is much more than only drawing causal loop diagrams.

I would have thought the situation you describe too complex for one, single causal loop to be illuminating. How are you defining the terms of your literature search? How is the problem defined?

Dear Michael,

Thank you for the answer you give to Kazuo. However, what do you mean exactly by the <'barbaric' dark ages of man>? Please explain it to me. Thank you again! Marc.

In view of your reply, was thinking some recent work we engaged with innovation ecosystems thinking in which we also relate to the ecological and material, maybe relevant, see

and see this one from Domenico:

I would recommend this one particularly if you are looking at environmental aspects of systems thinking in the social sciences:

Capra, F. and Luisi, P.L., 2014. The systems view of life: A unifying vision. Cambridge University Press.

You can find the first chapter online:

http://www.capracourse.net/wp-content/uploads/2015/12/Introduction.pdf

Thanks Víctor, will try to check it in the future.

Much of the STEM thinking you’ve listed is also present in the VisHal Arts. It seems the more a course relies on Project Based Learning, the more those domains cross and intersect. I would also add convergent thinking vs. divergent thinking. Best wishes!

Everything by Ostrom and Colleagues is a must-read, some are classics.

Maybe, it's work-- I think you should contact me and see if we can collaborate and create a better path. ann.stalter@wright.edu

Dear Kees,

First of all, thank you for your interesting question!

You know that the issue of MOPs is mainly discussed in literature of systems engineering. Probably, you are well aware of this point that there are a wide variety of methods and approaches to address the issue of MOPs and assess/measure the system performance. However, based on recent findings in systems engineering, any system can be characterized in terms of functional requirements (this point is particularly stated in Axiomatic Design (AD) theory. Due to your specific question, it is not necessary to mention that the system of interest can be characterized in terms of customer attributes in customer domain of the system, functional requirements in functional domain of the system, physical or design parameters in physical domain of the system, and process variables in process domain of the system). Functional requirements should be measurable and quantitative. Regarding each functional requirement, a respective target value with a specific tolerance interval is defined. Functional requirements are often random variable and, hence, there is some degree of uncertainties to these variables. For this reason, we need to utilize the probability theory to quantitatively measure the success level to meet the target value specified for each functional requirement within its respective designed tolerance interval. The value of these probabilities are reported as measure of performance for the system under consideration. Concerning this discussion, I have attached a paper to this answer as well.

Hops this helps!

Good luck!

Ali

Development of infrastructure, better city planning, increased investment in research etc are all appreciated by the society. But, without putting a check into population growth, or mass migration into urban Central Business District (CBD) areas, all these efforts are like filling a pot with a hole in the bottom. Besides investing in infrastructure building and R&D projects, people need to be educated regarding ill effects of population explosion and strict regulations need to be enacted. Cities need to be decentralized so that population density is evenly distributed throughout the city. Only then we can expect sustainable development in our society.

One is a fraud, the other isn't. Systems thinking, unless you went to MIT, is a cognitive paradigm and useful in virtually any endeavor. If you went to MIT or read Forrester, Sengé, Richmond, et al. then systems thinking is system dynamics. Meadows wrote some useful concepts, but she was the exception in that crowd. The thing they don't tell you in their books is that system dynamics is a very limited, almost useless modelling tool that looks really cool, but can't be modified easily to reflect change or non-linearity. In other words, both the bias and the variance of the model are very high. The verification may be reasonable, but the validity tends to be bad.

Some examples of this failure: Jay Forrester's World Dynamics. I encourage you to read the reviews such as "Measurement without data." Peak oil. This was a system dynamics model crafted at great expense by some really smart economists and petrol-heads and predicted world oil production would peak about 15 years ago. Didn't happen. In fact production is up far beyond the simulation. So, what good was the system dynamics simulation? not much.

For a good insight on what systems thinking really is and how it is useful, check the work of Russell Ackoff. He has some good lectures online. C. West Churchman was his graduate advisor I believe, also worth reading.

Hi, Ali. Knowledge Management (KM) seems to be in need of a decentralizing revolution for empowering individuals and self-organized groups to be based on networked autonomous personal KM devices and a personal discipline for collection, filtering and creative connection (among data, among people, and between people and data flows) (Levy, 2012). Although the mounting urgency for such a solution is apparent (Wiig, 2011; Gratton, 2012; Nielsen, 2012; Frey & Osborne, 2013; Bowles, 2014), the inherent complexities for devising it constitute ‘wicked’ problems (ill-defined; incomplete, contradictory, changing requirements; complex interdependencies) where the information needed to understand the challenges depends upon one’s idea or concept for solving them (Rylander, 2009).

System thinking and Design Science Research (DSR) guidelines (Hevner et al., 2004) aimed are essential to address knowledge assets and their dynamic relationships/processes as well as to integrate a varied scope of KM concepts and practices, such as associative indexing (Bush, 1945), cumulative synthesis (Usher, 1954), attention management (Simon, 1971), personal autonomy (Nonaka, 2000), aggregatable ‘nano’ performances (Wiig, 2011), or creative conversations (Levy, 2012).

The aim of a current DSR project is to develop such a novel KM concept supported by an innovative IT prototype system (that extend human and social capabilities and meet desired outcomes) and to validate the underlying system thinking and design science research processes (as evidence of their relevance, utility, rigor, resonance, and publishability). The continued non-availability of such personal KM systems and tools inhibits organizations (Pasher & Ronen, 2011), academia (Bedford, 2012), and the creative class (Florida, 2012) due to non-effective tools (Kahle, 2009; Davies, 2011), lacking work force acceptance of traditional KM system generations (Wilson, 2002; Schuett, 2003; Malhotra, 2004; Pollard, 2008; Frost, 2013), and escalating opportunity divides (Drori, 2010; Giebel, 2013).

very interesting

good luck

Hi Ashley, I am not sure if this is the text you are but the citations is below:

Davis, A. C., & Stroink, M. L. (2016). The relationship between systems thinking and the new ecological paradigm. Systems Research and Behavioral Science, 33(4), 575-586.

Missing data must be pointed out for accuracy of results . Actually missing data is of two types 1. system missing 2. User defined

For Non string variables any empty cell will be considered as missing data you dont need to declare

in case of user defined it needs to be declared go to data view column 7 missing and select the discrete missing values and enter the number of variable missing ,press ok . You will see them in variable view window now.

Local knowledge can be seen from different perspectives and embedded in different discourses. The word 'local' puts it in the immediate space for which plans are drafted. As such a local Urban Planner, who lives since long time in the respective city has such local knowledge, at least more of it than an urban planner who has been hired from far away. However, and some of the contributions argue in this direction, local can also mean people's knowledge in comparison to scientific knowledge.

Other expressions that at times appear in the discourses are 'traditional knowledge' and 'indigenous knowledge'. Of course there are slight, but very meaningful differences between all of these expressions, but what they might have in common is the assumption that beyond the scientific way of conceptualizing things there are also other qualities, which can add perspectives to the scientific view that are essential.

I just came back two week ago from doing research about 'traditional cyclone shelters in Vanuatu'. These shelters, which have survived the strongest cyclones ever are the result of 'local' knowledge. Aren't they? E.G. an important way o9f putting wooden logs together is by tying them together with wild vines instead of using nails. This flexibility provides stability as logs fixed by nails would disconnect soon when the power of heavy winds would slowly but surely loosen the connection.

Indeed I came across a similar principle many years ago in South India, where fishermen built boats by stitching the wooden planks together with coir. Also here the flexibility enhanced the stability when the boat went through the surf of the ocean.

‘Local knowledge’ here might be the knowledge which is built from experience, from being practically attached to something instead of reflecting and researching things.

Stein, D. S. (2002). Creating local knowledge through learning in community: A case study. New directions for adult and continuing education, 2002(95), 27-40.

Bishop, B. W. (2011). Location‐based questions and local knowledge. Journal of the Association for Information Science and Technology, 62(8), 1594-1603.

Aveling, E. L. (2011). Mediating between international knowledge and local knowledge: the critical role of local field officers in an HIV prevention intervention. Journal of Community & Applied Social Psychology, 21(2), 95-110.

Mammo, T. (1999). The paradox of Africa's poverty: the role of indigenous knowledge, traditional practices and local institutions--the case of Ethiopia. The Red Sea Press.

Sekhar, N. U. (2004). Local versus expert knowledge in forest management in a semi‐arid part of India. Land Degradation & Development, 15(2), 133-142.

Thomas, D. S. G., & Twyman, C. (2004). Good or bad rangeland? Hybrid knowledge, science, and local understandings of vegetation dynamics in the Kalahari. Land Degradation & Development, 15(3), 215-231.

Gerhardinger, L. C., Godoy, E. A., & Jones, P. J. (2009). Local ecological knowledge and the management of marine protected areas in Brazil. Ocean & Coastal Management, 52(3), 154-165.

Šūmane, S., Kunda, I., Knickel, K., Strauss, A., Tisenkopfs, T., des Ios Rios, I., ... & Ashkenazy, A. (2017). Local and farmers' knowledge matters! How integrating informal and formal knowledge enhances sustainable and resilient agriculture. Journal of Rural Studies.

Benham, C. F. (2017). Aligning public participation with local environmental knowledge in complex marine social-ecological systems. Marine Policy, 82, 16-24.

Shearmur, R., & Doloreux, D. (2008). Urban hierarchy or local buzz? High-order producer service and (or) knowledge-intensive business service location in Canada, 1991–2001. The Professional Geographer, 60(3), 333-355.

Yigitcanlar, T., O’connor, K., & Westerman, C. (2008). The making of knowledge cities: Melbourne’s knowledge-based urban development experience. Cities, 25(2), 63-72.

Yigitcanlar, T., & Velibeyoglu, K. (2008). Knowledge-based urban development: The local economic development path of Brisbane, Australia. Local Economy, 23(3), 195-207.

Skytt-Larsen, C. B., & Winther, L. (2015). Knowledge production, urban locations and the importance of local networks. European Planning Studies, 23(9), 1895-1917.

Pineda-Zumaran, J. (2016). Learning and knowledge generation in local decision making in the South: The case of urban infrastructure provision in Arequipa, Peru. Journal of Planning Education and Research, 36(1), 60-75.

Lau, U., & Seedat, M. (2015). The community story, relationality and process: Bridging tools for researching local knowledge in a peri‐urban township. Journal of Community & Applied Social Psychology, 25(5), 369-383.

The scopes of Design Thinking and Systems Thinking have expanded so much that they largely overlap now. The most concise answer is that Design Thinking focuses on synthesis, building up a solution to a problem, and Systems Thinking focuses on large-scale analysis, understanding a problem's many facets and dependencies, from technology to economics to people. Of course to do proper synthesis, you should have a system-wide understanding of the problem, so the two are firmly intertwined.

if you check the work of JC willems e.g IEEE Transactions on Automatic ControlVolume 36, Issue 3, March 1991, Pages 259-294 "Paradigms and Puzzles in the Theory of Dynamical Systems" or IEEE Control SystemsVolume 27, Issue 6, December 2007, Pages 46-99 "The Behavioral Approach to Open and Interconnected Systems"

it may give you an idea of the body of work available in this area. These are but a few, in deed Willems and team has done a lot of work in this....(esp the topic you asked on)

 Dear Ms. Wynne,

Thank you for your contribution!

Robert

Russell, I apologise for the lateness of my response - for some reason, I did not get any notification about your question. I am afraid Grounded Theory has a history of application in the field of education and technology.

Intelligent question Dr Senapati. I  feel , species diversity , either it is functional, compositional  or metabolic nature , they compliment each other at different levels of ecosystem functioning with varied implications , and always successful in better interpretation ....all a part of sustainable system..

As an Administrative Specialist I believe it is necessity indeed in finding proper solutions. Ofcourse it is found that the management is as a transdisciplinary study which tries to solve organizational problems.

By looking at the matrix issued by BCG, it is obvious that a transdisciplinary is conducted to offer the solutions.

It is very interesting that the Financial Balance is also existed in culture, policy, economy and society which was pointed in the book "Organizational Behavior" written by Robbins.

Mainz, Germany

Dear Ohri,

One way to look at the matter of machine learning and, as you put it, "analysis without reasoning," is that machine learning is mining the results of reasoning already embedded in the big data. If so, then it cannot reasonably be viewed as a substitute for reasoning such as informs the data.

One might entertain the hypothesis that there is a kind of "connection machine" architecture involved in lower cognition, but this ultimately functions to support the reasoning, rational faculties--which regularizes its results. In any case, the lesson would be that machine learning is no substitute for human reasoning, evaluation and formulation of theory, testing, definitions, etc.

H.G.Callaway

I think that such systems are not all that uncommon. For instance, there are systems that monitor the noise level of bearings, not because anyone might care about noise, but because bearing noise is easier to monitor than is bearing wear. So when a certain noise level and/or noise spectrum is reached, the monitoring system raises an alarm.

So, "If the specified threshold noise level/spectrum is measured, it must mean that bearing wear has reached x and must be replaced."

I suppose one could say the same thing about, say, monitoring the weather, or many other things. If the air pressure is x, and it is falling at a rate of y, then it must be true that we have a severe weather front approaching.

If this condition is true, then it must follow that this cause is true, going from effect to cause. Which may just be the easier direction to go, for practical reasons.

The modules make an important part of a purely mathematical model of physical reality. At the low end of the hierarchy are elementary modules that together configure all other modules. At the top are modular systems. On earth, the modules evolved in intelligent species. Elementary modules are elementary particles. The link below treats the model and contains 16 lectures. These lectures contain the required math.

I think so , but I need to control any works  in progress  in Canada .

Best wishes!

Riccardo

 “4 Industrial Revolution” appears like a continuity of the Copernican Revolution. With the Copernican Revolution we learn we were not at the center of the Universe, now we are going to learn what could be the consciousness, what could be a human being with regard to that of animals and so on. The result is that we put ourselves at distance, not exactly like an object, but at distance. Would that mean we are going to be able to establish the laws for life systems as we did with the laws of nature? At least, we know drastic changes are coming that will force us to think about ourselves in a different way.

Trying to put in perspective the huge development described in the Klaus Schwab’ article, it is worth recalling that we have to keep in mind human and animals need water and energy to stay alive. It is a question of priority, whatever the capability of the Big Data, AI or whatsoever, to save energy, it is a reasonable guess to suppose that the need of energy will steadily increase if we use the same economical reasoning that has dominated over the last 2 centuries.

Because, we now know that resources are not infinite, we now know that in our finite earth we have the capability to impact the future much beyond our finite lifespan, we must change our mind. We need to develop other practices that are not uniquely focus on solving questions at the local scale. We need to be much more than an augmented animal, if we are unable to do that we will not be able to develop strategies with long-term vision. For what we understand of human and animal metabolism, we are everything except a machine. We have a very poor yield, of about 5% if it makes sense to do such an evaluation, however we are still the most amazing achievement we know. A mystery we don’t know much about but are in situation to strongly modify.

At the end we refocus on people. We need to develop human networks, much more than virtual ones. We need to permanently build and rebuild human networks, the most efficient way to detect talented peoples and to reinforce their skillfulness. Improving the human relationship is at the heart of the creativity and the basic ingredient to enforce trust between peoples.

CRISPR Cas9 is part of a new instrumentation providing new strategies to question what could be information in life systems and how this information is processed. Unlike DNA computing, DNA origami platforms and others that aim at finding the fundamental rules that govern information based on a network of chemical reactions, CRISPR Cas9 is able to edit the genome. In other words, with CRISPR Cas9 we are able to process information in a life system without knowing what it is.

This is a usual way to go in research, the key issue with CRISPR Cas9 is that it addresses immediately the Animal and Human identities. It is going fast, we only start to understand what is the complexity at any scale that we are already in situation to modify the whole process at a large scale.

This is a complete new Ethical Question, because it is hard to envision the consequence at any level. Any Human actions contain implicitly the ability to correct and regulate wrong moves, even if the regulation might become expensive. It is not clear we could do a reverse move if needed. Indeed, we never do a reverse move but biased the orientation when we are doing wrong, therefore we need to find ways to keep this biased skill. This is very demanding and requires improving our human being and social organization. The key issue is trust, there are many possibilities to reinforce trust between people and to improve our human relationship capacity.

Just look at the existing overlapping constructs, identify the gaps and introduce yours. I have just investigated Social norm and its underlying internal/external dimensions. They both cannot serve as underlying dimensions yet they exist in the extant literature only to realise after long hustling and bustling. 

Have they done the CFA yet?

@Babak Bashirzadeh - Hi Bashir. Thanks for your offer. I see that you use Vensim. Could you please suggest on if Vensim can accept Python or Matlab coding ?

Thanks.

Thank you Christine for the list of articles, i cant waiting to start reading them. They will surely be of many help to me. I appreciate it.

One reference to add would be Nicholas Negroponte 's Soft architecture machines. I think Pask's contribution to this is where he first sets out P-Ind and M-Ind - its originally written around 1972 and so before the main conversation theory publications, although it only comes out in 1975. Negroponte's work would be a good example, even if he doesn't pick up all Pask's terminology, its within the same spirit (Pask was a consultant to Negroponte's lab).

I think its also worth looking back at Pask's devices before he came up with the P-Ind and M-Ind terminology. I think most of conversation theory is in Musicolour waiting to come out, and this might give you more flexibility on what you can cite.

*Negroponte, Nicholas. 1975. Soft architecture machines. Cambridge, MA: MIT Press.