Margaret Archer – Wikipedia

Thanks to Howard Silverman for flagging Professor Archer as a notable systems thinker!

Source: Margaret Archer – Wikipedia

 

Margaret Archer

From Wikipedia, the free encyclopedia

Margaret Scotford Archer (born 20 January 1943) spent most of her academic career at the University of Warwick, UK, where she was for many years Professor of Sociology. She was also a professor at l’Ecole Polytechnique Fédérale de Lausanne, Switzerland. She is best known for coining the term elisionism in her 1995 book Realist Social Theory: The Morphogenetic Approach. In April 2014, Professor Archer was named by Pope Francis to succeed former Harvard law professor and U.S. Ambassador to the Holy See Mary Ann Glendon as President of the Pontifical Academy of Social Sciences.[1]

She studied at the University of London, graduating B.Sc. in 1964 and Ph.D. in 1967 with a thesis on The Educational Aspirations of English Working Class Parents. She was a lecturer at the University of Reading from 1966 to 1973.

She is one of the most influential theorists in the critical realist tradition. At the 12th World Congress of Sociology, she was elected as the first woman President of the International Sociological Association, is a founder member of both the Pontifical Academy of Social Sciences and the Academy of Learned Societies in the Social Sciences. She is a Trustee of the Centre for Critical Realism.

She has supervised some Ph.D students, some of whom have gone on to contribute towards the substantive development of critical realism in the social sciences, including Robert Archer, author of Education Policy and Realist Social Theory[2], Sean Creaven, author of Marxism and Realism[3], and Justin Cruickshank, author of Realism and Sociology[4].

Analytical dualism[edit source]

Margaret Archer argues that much social theory suffers from the generic defect of conflation where, due to a reluctance or inability to theorize emergent relationships between social phenomena, causal autonomy is denied to one side of the relation. This can take the form of autonomy being denied to agency with causal efficacy only granted to structure (downwards conflation). Alternatively it can take the form of autonomy being denied to structure with causal efficacy only granted to agency (upwards conflation). Finally it may take the form of central conflation where structure and agency are seen as being co-constitutive i.e. structure is reproduced through agency which is simultaneously constrained and enabled by structure. The most prominent example of central conflation is the structuration theory of Anthony Giddens. While not objecting to this approach on philosophical grounds, Archer does object to it on analytical grounds: by conflating structure and agency into unspecified movements of co-constitution, central conflationary approaches preclude the possibility of sociological exploration of the relative influence of each aspect.

In contradistinction Archer offers the approach of analytical dualism.[5] While recognizing the interdependence of structure and agency (i.e. without people there would be no structures) she argues that they operate on different timescales. At any particular moment, antecedently existing structures constrain and enable agents, whose interactions produce intended and unintended consequences, which leads to structural elaboration and the reproduction or transformation of the initial structure. The resulting structure then provides a similar context of action for future agents. Likewise the initial antecedently existing structure was itself the outcome of structural elaboration resulting from the action of prior agents. So while structure and agency are interdependent, Archer argues that it is possible to unpick them analytically. By isolating structural and/or cultural factors which provide a context of action for agents, it is possible to investigate how those factors shape the subsequent interactions of agents and how those interactions in turn reproduce or transform the initial context. Archer calls this a morphogenetic sequence. Social processes are constituted through an endless array of such sequences but, as a consequence of their temporal ordering, it is possible to disengage any such sequence in order to investigate its internal causal dynamics. Through doing so, argues Archer, it’s possible to give empirical accounts of how structural and agential phenomena interlink over time rather than merely stating their theoretical interdependence.

Cellular automaton – Wikipedia

Source: Cellular automaton – Wikipedia

Vester’s Sensitivity Model

 

Sensitivity Model
Sensitivity Model Prof. Vester®The computerized planning- and management tool for complex systems.
In an more and more complex world the common methods of solving problems are no longer sufficient and therefore no longer appropriate. Prof. Frederic Vester, recognized expert in the field of cybernetics, has developed this instrumentarium to meet the demand.

Over 30 years the Sensitivity Model Prof.Vester®, since 2006 now further developed as “Malik Sensitivity Model®Prof.Vester”, has been successfully applied in the fields of:

  • management and technical consulting
  • business strategies
  • mediation
  • risk management
  • traffic planning
  • town- and regional planning
  • scientific research and education
  • ….

The Basis- and Professional Software SYSTEMTOOLS 9.2 for Windows XP, Vista, Windows 7, Windows 8 exists as trilingual software (German, English, Spanish). Further information on demand via email

Methodology, background and practical projects are described in Frederic Vesters book: The Art of Interconnected Thinking – Ideas and Tools for dealing with complexity. A New Report to the Club of Rome.
(MCB-Publishing House, Munich 2007, 2nd edition 2013). The book can be ordered online via Amazon.

Tel. +49 089-535010
info@frederic-vester.de
pdf
http://www.frederic-vester.de/uploads/InformationEnglishSM.pdf

The Centre for the Evaluation of Complexity Across the Nexus (CECAN)

Source: Welcome to CECAN | CECAN

 

The Centre for the Evaluation of Complexity Across the Nexus (CECAN), a £3m research centre hosted by the University of Surrey, is transforming the practice of policy evaluation in Nexus areas, to make it fit for a complex world.

CECAN is pioneering, testing and promoting innovative policy evaluation approaches and methods across Nexus domains such as food, energy, water and the environment, through a series of ‘real-life’ case study projects with co-funders (ESRC, NERC, DEFRA, BEIS, FSA and EA).

CECAN has been delivering a programme of evaluation methods workshops, training courses in evaluation tools and specialist seminars delivered by international experts, to encourage knowledge sharing and capacity building amongst those working in UK policy making.

 

 

Resources – https://www.cecan.ac.uk/resources?fbclid=IwAR053DvRhdfKIXMww1I_H6i4Eo4R8qAeGTo7qvcV0AfuPavFZP9gJNgdniE

 

 

CECAN is producing a variety of resources available for download from this page. They are all available under a Creative Commons CC BY 4.0 licence.

 

CECAN’s Updated Manifesto, April 2018 Version 2.0

CECAN Syllabus

CECAN Managing Conflicts of Interest

CECAN Toolkits

CECAN EPPNs

CECAN has launched a Policy and Practice Note Series, with e-versions available to download.

CECAN Project Reports

CECAN Annual Conference 2018 – Policy Evaluation for a Complex World

Presentations:

Posters:

CECAN E-Newsletter Archive

Other Resources

 

Barabási Lab

About Us

The Center for Complex Network Research (CCNR), directed by Professor Albert-László Barabási, has a simple objective: think networks. The center’s research focuses on how networks emerge, what they look like, and how they evolve; and how networks impact on understanding of complex systems.

To understand networks, CCNR’s research has developed to rather unexpected areas. Certain studies include the topology of the www – showing that webpages are on average 19 clicks form each other; complex cellular network inside the cell-looking at both metabolic and genetic networks; the Internet’s Achilles’ Heel. The center’s researchers have even ventured to study how actors are connected in Hollywood.

Source: Barabási Lab

General Morphological Analysis

General Morphological Analysis

A general method for non-quantified modeling

© Swedish Morphological Society, 2002 (Revised 2013)
Licensed under a Creative Commons Attribution.

Article presenting Fritz Zwicky’s General Morphological Analysis as a method for non-quantified modeling, scenario development and strategy analysis.

Source: General Morphological Analysis

 

 

Wikipedia: https://en.wikipedia.org/wiki/Morphological_analysis_(problem-solving)

 

Morphological Analysis by Fritz Zwicky

https://www.toolshero.com/creativity/morphological-analysis-fritz-zwicky/

 

 

 

 

 

 

A HANDBOOK of INTERACTIVE MANAGEMENT by John N. Warfield and A. Roxana Cárdenas

Click to access HIM_01.pdf

“RECURSIVE FRAME ANALYSIS: A Qualitative Research Method for Mapping Change-Oriented Discourse” by Hillary Keeney, Bradford Keeney et al.

Another free whole book

Source: “RECURSIVE FRAME ANALYSIS: A Qualitative Research Method for Mapping Ch” by Hillary Keeney, Bradford Keeney et al.

 DownloadDownload Full Text (5.2 MB)

Description

Recursive Frame Analysis (RFA) is a qualitative research method for mapping and analyzing change-oriented conversation. Cybernetician and therapist Bradford Keeney invented RFA over twenty years ago as a means of discerning and indicating the bare bones organization of real-time therapeutic performance. This book revisits some of Keeney’s original ideas while providing a more exhaustive theoretical foundation for RFA, a thorough exploration of its practical application as a research tool, and several detailed analyses of therapy sessions.

Rooted to Gregory Bateson’s notion of contextual frame and the way that a distinction can recursively operate on itself as formulated by G. Spencer-Brown’s Laws of Form, RFA offers both researchers and practitioners of all kinds a formal way of tracking the dramatological construction and movement of a conversation through its beginning, middle, and end episodes. By limiting the analysis to the actual performance of the conversation being studied – including spoken discourse and description of non-verbal action – RFA lays bare the primary distinctions, re-indications, and contextual frames embodied by the communication being studied, as well as those of the researcher. Commentary later generated by the researcher must be demarcated as a separate order of discourse, providing opportunity for multiple layers of analysis by researchers while keeping the primary data intact.

Though this book primarily exemplifies the application of Recursive Frame Analysis to the study of therapeutic sessions, RFA as a research tool is not limited to this domain but can be applied to the analysis of any change-oriented conversation, interaction, or even textual discourse to track the primary distinctions, recursively generated re-indications, and emergent contextual frames being constructed. It is intended that this book serve as a resource for the future application of RFA across multiple fields.

Publication Date

2015

Publisher

The Qualitative Report

City

Fort Lauderdale

Keywords

Recursive Frame Analysis, RFA

Disciplines

Community-Based Learning | Community-Based Research | Quantitative, Qualitative, Comparative, and Historical Methodologies | Social and Behavioral Sciences | Sociology

Comments

Hillary Keeney and Bradford Keeney would like to express their gratitude and appreciation to our colleagues in Mexico for their support of this work. In particular Pedro Vargas Avalos and Clara Haydee Solis Ponce, who with their colleagues have sponsored our teaching at the Department of Clinical Psychology, National University of Mexico (UNAM), Zaragoza, and Juan Carlos García and Sylvia Arce, who have sponsored our teaching at Etfasis: Institute of Systemic Family Therapy. It was during our seminars there that many of the ideas in this book were developed.

Ronald Chenail would like to thank President George L. Hanbury II and Nova Southeastern University for supporting The Qualitative Report; Hillary and Brad Keeney for their continuing guidance and encouragement; Lydia Acosta, Michele Gibney, and Cheryl Ann Peltier-Davis for launching NSU Works and helping with our first book development and release; Melissa Rosen for her copyediting and graphics skills; Adam Rosenthal for his leadership in making TQR Books a reality; and Jan Chenail, my late wife, for making me a better writer, husband, father, and friend.

Copyright 2014: Hillary Keeney, Bradford Keeney, Ronald Chenail and Nova Southeastern University

Strategic Assumption Surfacing and Testing (SAST)

csl4d's avatarCSL4D

Mitroff’s ‘operationalization’ of Churchman’s systems approach, part 1

In table 12-3 (p. 301 of Mason’s and Mitroff’s ‘Challenging strategic planning assumptions’) major approaches to business problem solving are compared, including the systems approach and SAST (strategic assumption surfacing and testing), but also analytic modelling (typical of operations research), the case method (widely used, but lacking in objectivity), structured approaches (e.g. PIMS and its many derivatives, often failing to look at key non-quantifiables). The problem with the systems approach is that it is difficult to operationalize (although it could be argued that Wicked Solutions solved that problem). The problem of SAST may be the unwillingness of participants to lay bare their assumptions. This is a general problem in all approaches where we want to leave no stone unturned (as assumptions, e.g. about people’s motivations, lurk beneath them). In this post I will argue that SAST can be combined with…

View original post 1,478 more words

Introduction to the Modeling and Analysis of Complex Systems – Open SUNY Textbooks

full, free textbook!

 

Source: Introduction to the Modeling and Analysis of Complex Systems – Open SUNY Textbooks

 

Introduction to the Modeling and Analysis of Complex Systems

 

Download PDF PDF DOWNLOAD  19 MB

Author(s): 

Keep up to date on Introduction to Modeling and Analysis of Complex Systemsat http://bingweb.binghamton.edu/~sayama/textbook/!

Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.

This textbook is available for purchase in both grayscale and color via Amazon.com and CreateSpace.com.

REVIEWS:

Hiroki Sayama’s book “Introduction to the Modeling and Simulation of Complex Systems” is … a unique and welcome addition to any instructor’s collection. What makes it valuable is that it not only presents a state-of-the-art review of the domain but also serves as a gentle guide to learning the sophisticated art of modeling complex systems. –Muaz A. Niazi, Complex Adaptive Systems Modeling 2016 4:3

 

… Sayamaʼs book is a very good instrument for students who want to read an introductory text on modeling and analysis of complex systems, and for instructors who need such a text in simple language for their complex systems courses and projects. The book offers a good introduction to the complex systems terminology and plenty of readily available examples with technical implementation details. … Overall, Introduction to the Modeling and Analysis of Complex Systems offers a novel pedagogical approach to the teaching of complex systems, based on examples and library code that engage students in a tutorial-style learning adventure. It is a solid tool that may become one of the primary instruments for teaching complex systems science and help the discipline to become more established in the academic world, triggering the necessary transition from a top-down tradition to a bottom-up complex systems approach.
-Stefano Nichele, Artificial Life 22(3): 424-427, 2016. www.mitpressjournals.org/doi/abs/10.1162/ARTL_r_00209

 

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I Preliminaries

1 Introduction

1.1 Complex Systems in a Nutshell

1.2 Topical Clusters

2 Fundamentals of Modeling

2.1 Models in Science and Engineering

2.2 How to Create a Model

2.3 Modeling Complex Systems

2.4 What Are Good Models?

2.5 A Historical Perspective

II Systems with a Small Number of Variables

3 Basics of Dynamical Systems

3.1 What Are Dynamical Systems?

3.2 Phase Space

3.3 What Can We Learn?

4 Discrete-Time Models I: Modeling

4.1 Discrete-Time Models with Difference Equations

4.2 Classifications of Model Equations

4.3 Simulating Discrete-Time Models with One Variable

4.4 Simulating Discrete-Time Models with Multiple Variables

4.5 Building Your Own Model Equation

4.6 Building Your Own Model Equations with Multiple Variables

5 Discrete-Time Models II: Analysis

5.1 Finding Equilibrium Points

5.2 Phase Space Visualization of Continuous-State Discrete-Time Models

5.3 Cobweb Plots for One-Dimensional Iterative Maps

5.4 Graph-Based Phase Space Visualization of Discrete-State Discrete-Time Models

5.5 Variable Rescaling

5.6 Asymptotic Behavior of Discrete-Time Linear Dynamical Systems

5.7 Linear Stability Analysis of Discrete-Time Nonlinear Dynamical Systems .

6 Continuous-Time Models I: Modeling

6.1 Continuous-Time Models with Differential Equations

6.2 Classifications of Model Equations

6.3 Connecting Continuous-Time Models with Discrete-Time Models

6.4 Simulating Continuous-Time Models

6.5 Building Your Own Model Equation

7 Continuous-Time Models II: Analysis

7.1 Finding Equilibrium Points

7.2 Phase Space Visualization

7.3 Variable Rescaling

7.4 Asymptotic Behavior of Continuous-Time Linear Dynamical Systems

7.5 Linear Stability Analysis of Nonlinear Dynamical Systems

8 Bifurcations

8.1 What Are Bifurcations?

8.2 Bifurcations in 1-D Continuous-Time Models

8.3 Hopf Bifurcations in 2-D Continuous-Time Models

8.4 Bifurcations in Discrete-Time Models

9 Chaos

9.1 Chaos in Discrete-Time Models

9.2 Characteristics of Chaos

9.3 Lyapunov Exponent

9.4 Chaos in Continuous-Time Models

II Systems with a Large Number of Variables

10 Interactive Simulation of Complex Systems

10.1 Simulation of Systems with a Large Number of Variables

10.2 Interactive Simulation with PyCX

10.3 Interactive Parameter Control in PyCX

10.4 Simulation without PyCX

11 Cellular Automata I: Modeling

11.1 Definition of Cellular Automata

11.2 Examples of Simple Binary Cellular Automata Rules

11.3 Simulating Cellular Automata

11.4 Extensions of Cellular Automata

11.5 Examples of Biological Cellular Automata Models

12 Cellular Automata II: Analysis

12.1 Sizes of Rule Space and Phase Space

12.2 Phase Space Visualization

12.3 Mean-Field Approximation

12.4 Renormalization Group Analysis to Predict Percolation Thresholds

13 Continuous Field Models I: Modeling

13.1 Continuous Field Models with Partial Differential Equations

13.2 Fundamentals of Vector Calculus

13.3 Visualizing Two-Dimensional Scalar and Vector Fields

13.4 Modeling Spatial Movement

13.5 Simulation of Continuous Field Models

13.6 Reaction-Diffusion Systems

14 Continuous Field Models II: Analysis

14.1 Finding Equilibrium States

14.2 Variable Rescaling

14.3 Linear Stability Analysis of Continuous Field Models

14.4 Linear Stability Analysis of Reaction-Diffusion Systems

15 Basics of Networks

15.1 Network Models

15.2 Terminologies of Graph Theory

15.3 Constructing Network Models with NetworkX

15.4 Visualizing Networks with NetworkX

15.5 Importing/Exporting Network Data

15.6 Generating Random Graphs

16 Dynamical Networks I: Modeling

16.1 Dynamical Network Models

16.2 Simulating Dynamics on Networks

16.3 Simulating Dynamics of Networks

16.4 Simulating Adaptive Networks

17 Dynamical Networks II: Analysis of Network Topologies

17.1 Network Size, Density, and Percolation

17.2 Shortest Path Length

17.3 Centralities and Coreness

17.4 Clustering

17.5 Degree Distribution

17.6 Assortativity

17.7 Community Structure and Modularity

18 Dynamical Networks III: Analysis of Network Dynamics

18.1 Dynamics of Continuous-State Networks

18.2 Diffusion on Networks

18.3 Synchronizability

18.4 Mean-Field Approximation of Discrete-State Networks

18.5 Mean-Field Approximation on Random Networks

18.6 Mean-Field Approximation on Scale-Free Networks

19 Agent-Based Models

19.1 What Are Agent-Based Models?

19.2 Building an Agent-Based Model

19.3 Agent-Environment Interaction

19.4 Ecological and Evolutionary Models

Bibliography

Index

Hiroki Sayama

Hiroki Sayama, D.Sc., is an Associate Professor in the Department of Systems Science and Industrial Engineering, and the Director of the Center for Collective Dynamics of Complex Systems (CoCo), at Binghamton University, State University of New York. He received his BSc, MSc and DSc in Information Science, all from the University of Tokyo, Japan. He did his postdoctoral work at the New England Complex Systems Institute in Cambridge, Massachusetts, from 1999 to 2002. His research interests include complex dynamical networks, human and social dynamics, collective behaviors, artificial life/chemistry, and interactive systems, among others. He is an expert of mathematical/computational modeling and analysis of various complex systems. He has published more than 100 peer-reviewed journal articles and conference proceedings papers and has edited eight books and conference proceedings about complex systems related topics. His publications have acquired more than 2000 citations as of July 2015. He currently serves as an elected Board Member of the International Society for Artificial Life (ISAL) and as an editorial board member for Complex Adaptive Systems Modeling (SpringerOpen), International Journal of Parallel, Emergent and Distributed Systems (Taylor & Francis), and Applied Network Science (SpringerOpen).

are there any developed methods specific to #complexitytheory other than Agent Based Modelling?

I asked this question on social media and in the systems thinking facebook groups (#lazyweb – all those I could address with a single click through buffer.com).

I think my emergent point is that any real distinction is purely tactical/motivated/arbitrary – at the very least that the overlaps between ‘cybernetics’, ‘systems thinking’, and ‘complexity science’ are so massive – and have such shared routes – that, in order to carve any of them out as individual territories, you have to artificially apportion stuff that rightfully belongs to one or both to the other… if you see what I mean! Remember that I am trying to think specifically about *methods*.

Each has some elements which are of course distinct – agent-based modelling seems like the best candidate in ‘complexity’ – and certainly specific *applications* of mathematical techniques – and maybe some stuff around network modelling?

Here are the threads:

https://www.facebook.com/groups/774241602654986/2083395748406225/?comment_id=2083432968402503&notif_id=1549119341857033&notif_t=group_comment&ref=notif

https://www.facebook.com/groups/2391509563/10156785597314564/?comment_id=10156785650364564&notif_id=1549119885345223&notif_t=group_comment&ref=notif

https://www.facebook.com/groups/1698754760335916/2275321949345858/?comment_id=2275328329345220&notif_id=1549119105597714&notif_t=group_comment&ref=notif

There will now follow a lot of posts of interesting approaches unearthed!

seeking amazing speakers in London and Manchester for Systems and Complexity in Organisation

I’m a non-exec Director of www.scio.org.uk – systems and complexity in organisation – and we run four open days a year, with four systems-related speakers at each one.

We would love to have more speakers (and attendees) related to systems change, regenerative agriculture, sustainability, philanthropy, international development etc. We’d also love to have more contributors from sectors we hear less from – health, education at all levels, social care, arts, charities

Can you recommend any speakers – would you be interested to speak? Reply here or drop me a line at ben.taylor@scio.org.uk

Cheers!
Benjamin

Definitions of systems and systems thinking

csl4d's avatarCSL4D

There are no simple definitions of systems and systems thinking (Monat & Gannon 2015) that are sufficiently rich to clarify what they are essentially about. So instead, I will offer a circumscriptive definition in three parts and add a small concept map to go with it.

Systems thinking       …. is the selection and application of more or less general systems methods or systemic problem solving tools to examine, debate, model, and modify systems structures, which underlie systems behavior. Systems thinking serves to identify and improve or understand the system behavior of a broad range of open systems.

Open systems      … consist of sets of at least two parts, elements, components or subsystems that are characterized by at least one interrelationship. The distinction between an open system and its environment is conceptualized by the system boundary. Open systems interact with their environment by receiving…

View original post 295 more words

Requisiteagility – Requisite Agility UnSymposium, 9-10 Feb 2019, New York City

Well, I am bemused and mystified how I managed not to see this until a week before the event! And I’m intrigued. The combination of Requisite Organisation, Viable Systems Model, and Agile is a powerful one. And of course each of them is very different, though each has been proven to be valuable in practice, when done well.

A few thoughts:

  • I have wondered for a while about when and how agile would be absorbed into the deeper theoretical tradition of management and systems thinking (in a meaningful/useful way, I don’t mean McKinsey or HBR), but it has largely remained focused on (a) practice and (b) competing brands and commercial offers to date.
  • This is an unconference (plus speakers), but ?every speaker is listed with their own branded ‘offer’, which might rather illustrate the problem.
  • I can’t help thinking that Viable Systems Model might have come in there later because someone said ‘oi! there’s a theory which focuses entirely on viability as requisite agility, you know’. I would have said that the use of ‘requisite’ and the entire thrust of the theories are ‘orthogonal’ between RO and VSM – though I explicitly merge them (or at least learning from Elliot Jacques through various routes), and Luc Hoebeke has done it theoretically as well (shame he’s not speaking!)
  • These kind of blendings can be the most sterile, or the most fertile, depending on how well the incommensurability of different traditions can be addressed!

Charter: https://requisiteagility.org/wp-content/uploads/2018/10/racharter.pdf

 

In the about page, it says:

Provenance of RA


The idea of Requisite Agility (RA) was born in 2009, when Amit Arora was in Russia for a software development project based on the Requisite Organization (RO) theory. This was the first time he was introduced to RO and its concepts, denoting the start of a journey of contextualizing the Requisite Organization concepts with the concepts of agile/agility.
**
From 2014 onwards, in collaboration with Steve Clement and many other contributors (see the list below), the idea of Requisite Agility has gradually evolved into a coherent conceptual foundation that recasts many RO concepts into today’s complex and agile context, provides a well-founded scaffolding to design and implement business agility, and is agnostic to any best practice method or framework.

As of the end of 2018, the RA concept is ready for a major leap: collaborative elaboration of the theory, tenets and terminology. To this end, an UnSymposium will be held in early 2019 to get together the harbingers, proponents and future practitioners of RA. The results of the RA community will be released as creative commons open content for anyone to apply.

 

 

Main page, source: Requisiteagility – Requisite Agility UnSymposium

 

About RA


Requisite Agility™ (RA) refers to  the organization design principles, patterns and practices that enable the organization to effectively adapt, responsively or preemptively, to changes in its environment. It provides a constructive scaffolding that helps the organization sense these changes, make sense of them, and act accordingly, while learning from the experience.

RA is agnostic to any specific agile methodology, best practice, or framework. Drawing on the systems and complexity theories, it entails theoretically informed and organization-specific diagnosis, analysis, design and implementation of organizational structures, processes and practices that are adaptive in their context.

RA operationalizes key Agile Properties

Driving Questions


Systems Design: Because everything* is systems – Alëna Louguina

Source: Systems Design: Because everything* is systems – Shopify UX

 

Systems Design: Because everything* is systems

This is a computer generated image of Stereocilia, the sensing organelles of hair cells found in the inner ear. They respond to motion for various functions, including hearing and balance inside human body. The hair cells turn the fluid pressure and other stimuli into electrical signals that travel to the brain where they are interpreted as sound. This image also looks like an extra-terrestrial landscape. *Note: ‘everything’ that is a set of connected things or parts forming a complex whole. For example, a pile of saw-dust is not a system, and a wooden dining table is.

The Warning of the Doorknob

All good product and service design begins with systems analysis. We are tasked with understanding not just one system that the product will be composed of (e.g. electrical, information, mechanical, hydraulic, etc.) or is designed for (transportation, manufacturing, social, natural, etc.) but many. Systems are fascinating but also a great source of anxiety, because they are uncertain, full of complex interpersonal relationships, indefinite, and difficult.

This anxiety is rooted in the hierarchical nature of systems and moves in two directions, infinite escalation and regression.

Have you ever wondered why we have such high resolution photos of space and still terribly grainy photos of cells and molecules?

John P. Eberhard, a neuroscientist who studies brain and how it experiences built environments, named this dichotomy of complexities “the warning of the doorknob”, described by Ed Yourdon in his book ‘Just Enough Structured Analysis’:

This has been my experience in Washington when I had money to give away. If I gave a contract to a designer and said, “The doorknob to my office doesn’t have much imagination, much design content. Will you design me a new doorknob?” He would say “Yes,” and after we establish a price he goes away. A week later he comes back and says, “Mr. Eberhard, I’ve been thinking about that doorknob. First, we ought to ask ourselves whether a doorknob is the best way of opening and closing a door.” I say, “Fine, I believe in imagination, go to it.” He comes back later and says, “You know, I’ve been thinking about your problem, and the only reason you want a doorknob is you presume you want a door to your office. Are you sure that a door is the best way of controlling egress, exit, and privacy?”

“No, I’m not sure at all.” “Well I want to worry about that problem.” He comes back a week later and says, “The only reason we have to worry about the aperture problem is that you insist on having four walls around your office. Are you sure that is the best way of organizing this space for the kind of work you do as a bureaucrat?” I say, “No, I’m not sure at all.” Well, this escalates until (and this has literally happened in two contracts, although not through this exact process) our physical designer comes back with a very serious face. “Mr. Eberhard, we have to decide whether capitalistic democracy is the best way to organize our country before I can possibly attack your problem.”

On the other hand is the problem of infinite regression. If this man faced with the design of the doorknob had say, “Wait. Before I worry about the doorknob, I want to study the shape of man’s hand and what man is capable of doing with it,” I would say, “Fine.” He would come back and say, “The more I thought about it, there’s a fit problem. What I want to study first is how metal is formed, what the technologies are for making things with metal in order that I can know what the real parameters are for fitting the hand.” “Fine.” But then he says, “You know I’ve been looking at metal-forming and it all depends on metallurgical properties. I really want to spend three or four months looking at metallurgy so that I can understand the problem better.” “Fine.” After three months, he’ll come back and say, “Mr. Eberhard, the more I look at metallurgy, the more I realize that it is atomic structure that’s really at the heart of this problem.” And so, our physical designer is in atomic physics from the doorknob.

That is one of our anxieties, the hierarchical nature of complexity.

Illustration of Dr. Eberhard’s design dilemma of escalation and regression in today’s complex systems (author: Alëna Iouguina)

Now we must not forget that Dr. Eberhard had his own complexity to deal with. From the perspective of escalation, he moves from being an H. sapiens in an urban habitat to a temperate climate zone of New York to our planet’s biosphere. And in another direction, Dr. Eberhard is composed of …

Dr. Eberhard has his own H. sapiens dilemma to deal with (author: Alëna Iouguina)

All these systems talk to each other. While Dr. Eberhard is dealing with his door handle, environmental agencies are trying to figure out how to revert the harmful effects of out-of-control manufacturing systems on Earth’s biosphere. And atoms of a door handle are peacefully swerving in the void along with the atoms of Dr. Eberhard’s hand.

Bridging the Distance Between Fundamental Rules and Final Phenomena

Years ago, still a design student, I peeked into John Buschek’s cozy office on the grounds of a project I was working on and ended up in one of his remarkably comfortable chairs, being questioned about the entire purpose of my project. He concluded:

“Designers often jump to a solution before ever asking a question of why the solution is needed in the first place. Don’t worry, scientists do it too.”

And he isn’t talking about a product or service, but more about the fundamental question of systems surrounding it. As a chemist, John engaged in many well funded endeavours of “creating chemistry for the sake of creating chemistry”. He called it ‘mundane science’. There is an excellent paper by Daniel Kammen and Michael Dove titled The Virtues of Mundane Science:

The prejudice against research on mundane topics has created a conceptual “cordon sanitaire” within many disciplines. In energy and development research, it appears as a disproportionate focus on advanced combustion systems, commercial fuels, and large centralized power facilities, even though more than 3 billion people rely on wood, charcoal, and other biomass fuels for the bulk of their energy needs.

Major obstacles to developing sound environmental practices are not principally technological. Instead, the primary stumbling block is the lack of integrative approaches to complex systems and problems. A mundane example — efforts to improve wood and charcoal burning cookstoves — illustrates the important advances that are possible from integrating scientific, engineering, and social science research with very practical implementation programs.

Here it is: lack of integrative approaches to complex systems and problems. There is a great video narrated by Richard Feynman, titled Curiosity:

My favourite phrase from the video:

“So much distance between fundamental rules and final phenomena.”

We all know about emergent behaviour, spontaneous organization, and collective decisions. But what do we really know about the rich web of interactions within all these phenomena?

John Holland once elegantly compared economic system to a natural one:

There is no master neuron in the brain nor is there a master cell within a developing embryo. If there is to be any coherent behaviour in the system, it has to arise from competition and cooperation among the agents themselves.

This is quite true in the economy as well: regardless of how much CEOs of companies are trying to cope with a stubborn recession, the overall behaviour of the economy is still the result of myriad economic decisions made every day by millions of individual people.

More importantly, it’s all about never-ending reshuffling and rearrangement of the said neurons and cells to ensure the resiliency of the system. Thus, an integrative approach and continuous questioning might open a designer’s mind to the world of heavy, complex problems that need solving; beyond safe, easily-accessible, peddled, and ubiquitous challenges that are so tempting to quickly tackle.

And the best part, the more you dive into a complex problem the more you start connecting the dots, branching out, and converging again. This brings about a whole array of inspirations and allows a designer to truly understand the space they are designing for, instead of generalizing and assuming its purpose and function. And, of course, the more we learn, the more humble we become. And isn’t that what designer should strive to be? As Richard Dawkins put it:

The world and the universe is an extremely beautiful place, and the more we understand about it the more beautiful does it appear. It is an immensely exciting experience to be born in the world, born in the universe, and look around you and realize that before you die you have the opportunity of understanding an immense amount about that world and about that universe and about life and about why we’re here.

We have the opportunity of understanding far, far more than any of our predecessors ever. That is such an exciting possibility, it would be such a shame to blow it and end your life not having understood what there is to understand.


After publishing this article, Timi Olotu wrote a thoughtful comment that turned out to be an eloquent summary to my often serpentine thoughts. So, I’m adding it here:

Your discussion of the anxiety attached with the concepts of infinite regression and escalation is basically a physical exploration of the metaphysical concept of an “existential crisis”.

An existential crisis, in other words, is a self-aware “system” attempting to identify which part of the existential spectrum it uniquely occupies… but failing and getting lost in either infinite regression or escalation.We identify systems as occupying a unique part of the existential spectrum based on where one system (or series of systems) ends and where another begins — i.e. part of knowing that a rock is a rock comes from knowing that it is not the same as sand (or some other thing).

This logic (in small doses) is also essential when solving complex problems of any kind […]. It’s a reminder for designers to neither live in blissful ignorance nor suffer a “design existential crisis” — to be aware of the complexity of systems but not get lost in them.

Finally, the art of murmuration to illustrate the difference between non-system and a system!


The best part about systems is, despite their complexity, they can be understood. Stay tuned for the follow-up article that will introduce methods of systems analysis and design.