Scientific method: fundamental truth vs. fluid knowledge

In the discussion of Planck and Mach, it should be considered that Mach was an empiricist, who had partly auto-didactically trained himself before his formal education and furthermore trained in a handicraft as a woodworker, while Planck was of a theoretical bend, declining for himself the 'need' to do empirical research. This seems important as Mach brings the experience of trial and error, tinkering or 'bricolage' to his theoretical and metaphysical views of (the development of) science.

Planck disagrees with his interpretation of Mach's historio-critical view of science at the beginning of his (Planck's) "Survey of Physics" because Mach tries to build his concepts of (physical) science on the notion of fluidity of human knowledge and the limits of models made up by (wo)man, which Mach sees as a currently 'valid' thought economic conceptualization of the known facts (and supporting assumptions resp. theories). Planck understands these as 'more or less arbitrary' constructions (which, I think, they are not as they are historically developed and take account of the currently known facts 'arranged' under the needs of specific world-views of scientists).



Furthermore, Planck disagrees with Mach's basing science in sense-perceptions (which are nevertheless deemed a useful starting point and correction to former exaggerations based on physical research results), but favors a view based on the 'constancy' of the properties of reality, a constancy which persists through all individual and historical interpretative variation. If I am not mistaken (pls. correct if I am wrong) Planck favors a statistical approach to (re-)searching these constant properties of reality, e.g. endorsing Boltzmann's thermodynamics in this context.



(N.B.: To me it seems Planck reduces the notion of Mach's perception complexes, which link properties of reality and their representation in the mind through mutually dependent functional complexes. Even if we use 'modern', extended sense-organs such as microscopes and Large Hadron Colliders, these measurement instruments can, just like our senses, only react to and register what they are 'designed' and constrained to capture - which leads us to issues with particle-wave dualistic appearances of entities.)



In contrast, Mach uses the 'historic-critical method' developed by religious scholars at the University of Tübingen, who put statements from the bible in their historical context, see e.g. his "Science of Mechanics. A Critical and Historical Account of its Development."

 As indicated above, Planck sees the world differently, where the standard of scientific research should be to strive for 'a fixed world-picture independent of the variation of time and people'. He argues that (physical) theory can be built on more 'fundamental' and unchanging concepts such as his Planck constant. Science should (can?) derive fundamental, stable statements or truths in his view.



I do see how Planck's approach works for abstract constructs as theoretical *pictures of the world* - one can build axiomatic, theoretical systems that capture (or seem to coincide with in a more critical reading) observed facts. However, I do not see how this works on a larger scale of several generations of practical researchers and theorists building their theoretical systems based on what they deem fundamental facts - which usually tended to change over the human history of science (which leaves us with the question: are Planck's constants and constancies subject to change?).



Conference "An Ecology of Ideas" by American Society for Cybernetics and Bateson Idea Group

From the website:

The American Society for Cybernetics (ASC) and Bateson Idea Group (BIG) come together to hold a conference on the relations among ideas as seen from multiple perspectives. We come from many disciplines but have common roots including cybernetics, circularity, reflexivity, language, culture and systems. For many of us these roots are enmeshed with biology, information, pattern, design, art, aesthetics, ethics and more.

In a world rife with factionalism and disenchantment, we will engage in conversations to integrate disciplines of knowing while taking into account our histories and considering our futures. We will regard both the parts and the whole that arises from the relations between the parts — and thus becomes the context for all the parts. We are concerned with the world that arises from how we live our ideas.
ASC and BIG have common interests in dynamic systems of thought, wisdom and learning. We accept that there are many views and value exploring the relationships between them, rather than in insisting that any view is “right”.

Here is the link to the website and the Call for papers

Conference: The Law & Economics of Organization: New Challenges and Directions

The Walter A. Haas School of Business, with support from the Alfred P. Sloan Foundation, is issuing a call for original research papers to be presented at the Conference on The Law & Economics of Organization: New Challenges and Directions. 

The purpose of the conference is to take stock of recent advances in the analysis of economic organization and institutions inspired by the work of 2009 Nobel Laureate Oliver Williamson and to examine its implications for contemporary problems of organization and regulation. Empirical research and research informed by detailed industry and institutional knowledge is especially welcome. 

Paper proposals or, if available, completed papers should be submitted on line at http://www.bus.umich.edu/Conferences/Haas-Sloan-LEO-Conference by March 31, 2012. The deadline for completed papers is November 1, 2012. Selections will be made by the conference organizers, Professors Pablo Spiller (Berkeley), Scott Masten (Michigan), and Alan Schwartz (Yale). Conference papers will be published in a special issue of the Journal of Law, Economics, & Organization.

International Summit and Conference on Enterprises *as* Systems

For the past 6 years the International Conference on Enterprises as Systems: Theory and Theory in Action has been concerned with the treatment of (networked) enterprises *as* systems in constantly changing social, economic, legal and technical environments. It has been held (with varying degrees of success) with the intent to create an environment for the collaborative exchange of knowledge among and between the Systemics Community, the Systems Architecture and Engineering Community (including Enterprise Architecture), and communities that are concerned with any aspect/part or whole of (inter-, intra-) enterprise systems and enterprises *as* systems.

This year, to further the collaboration effort, a(n) (Networked)Enterprises *as*
Systems Summit will be held in conjunction with the conference. Common threads for both the summit and conference include:

(1) (networked)enterprises *as* systems in the (general) systems-theoretic sense (systemics and the systems family of disciplines)
(2) identification and characterization of some of the most complex problems facing (networked) enterprises and potential solutions to which systemics may contribute.
(3) the formal and/or empirical representation of such systems for description,explanation, simulation, prediction and operation(formal/empirical theory)
(4) the use/application of theory in analysis / design, architecture / engineering, strategy, tactics, and operation of (inter-, intra-) enterprise systems and enterprises *as* systems

In general an enterprise may be considered a business, an educational organization, a standards body, a government organization, a federation, a group of enterprises bound by law in some fashion, any group of cooperating / collaborating enterprises such as those in GRID systems and emergency management/response systems, etc. In essence, an enterprise is a socio-technical system with dynamically varying systems characteristics which are dependent on both its' internal environment and its' external social, legal, economic/financial, and technical environment.

Disciplines of Systemics include, but are not limited to: General Systems Theory, Complex Adaptive Systems, Cybernetics, System-of-Systems, Systems Dynamics, Systems Thinking, Systems Engineering, Systems Analysis, Autopoiesis, Organization Theory.

The distinction between the summit and conference is that a summit organization committee, with input from interested individuals/organizations, will determine the summit tracks, the summit schedule, duration, etc.
The conference (and workshop) will focus primarily on a special topic and individuals will submit contributions for consideration. It is tentatively planned for the conference to kick-off the summit the first week in Aug. The summit will be virtual and will tentatively last until the third week in Nov.

Conference Dates: August 15-17, 2012
The Summit will run from August 20 - November 21st.

Summit planning is in the early stages. If anyone has any comments/questions, or if any person or organization is interested in planning the *summit* please contact me.

Conference information may be found at: http://www.enterprisesystemtheory.net/estconference/estconference.html.

The Science of Complexity: Understanding the Global Financial Crisis

Business-oriented symposium by interesting combination of two 'complexity' institutions: 

The time-honored formulas of mainstream economics no longer capture the complex dynamics of today's financial markets. This three-day symposium offers a view of the recent global financial crises from a new perspective—that of complexity science. Sponsored by two leading complexity research institutes, the symposium will feature several of the world's most prominent complex systems thinkers. These experts will offer insights from non-linear dynamics, social networks, systemic risk, experimental economics, computational social science, and other areas that are vital not only to understand the current crises but to develop policies that address the underlying causes.

The program is open to any interested participants, but is particularly designed for professionals in government, business, and the non-profit sectors. 

May 16-18, 2012, at the new Founders Hall facility at the GMU Arlington, VA campus

For more information see http://krasnow.gmu.edu/soc.

Scientific practice and scientific progress: Integration and testing of rival hypotheses

In school I still learned that it is good argumentative practice (and a better strategy) to deal with potential counter arguments and criticisms by taking them apart in the course of your argument. Science is about making decisions about rival hypotheses, interpretations of evidence based on a set of observations, tests, experiments (depending on in which field you work and what is feasible). Thus, it is good scientific practice to compare rival hypotheses / theories (i.e. systems of interpretation of facts, data, or other evidence), by testing them with a set of data.

Rival hypotheses might (or rather often are indeed) special cases of an underlying reality. There is some truth in all observations, some are better than others, some are more suitable than others given a specific context. Think of the parts of an elephant that is examined by several blind men. Everyone comes up with different observations and theories about “reality”. 

That is what we actually often can observe in scientific practice: ideologically 'blinded' representatives of schools of thoughts mindlessly hurling arguments about “reality” at each other – based on selective interpretations of data – without looking for an integrative theory. 

Thus systems of scientific thought have the ability to press observations into procrustes beds that seem to lead to different “proven” true interpretations of reality – which are thus artifacts of more or less subtle differences in scientific worldviews, i.e. perspectives on the underlying reality .

Integration of opposing, rival views can be achieved if scientists (just as “ordinary” people) are able to switch their perspective and manage to develop dialectically the synthesis from thesis and antithesis. A set of literary examples that nicely shows how the integrative method works are Arthur Conan Doyle’s stories about Sherlock Holmes. Holmes generates a number of partial hypotheses based on the integration of facts known so far - which are proven 'wrong' by the some new detail until he stumbles across the truth by some coincidence. 

(Arthur Conan Doyle was incidentally influenced by Charles S. Peirce’ pragmatist philosophy, which stresses abduction (something akin to intuition) as source of knowledge. I cannot claim to be an expert on Peirce, but my understanding of Peirce’ abduction is that it is this process of generating new knowledge by integrating controversial elements into a larger picture.)

Resurrected and revised post from the Organization and Markets blog a few years ago.

Complexity Science and Social Science At the Interface to the Real World

Call for Papers and Conference Participation

Coping with the global-scale challenges of financial instability, food security, climate change, sustainability, demographic change and migration, pervasive web technology, transnational governance and security, among others, will involve dealing with large-scale complex systems made up of many parts interacting and adapting in sometimes subtle ways. People are critically important components of them all, which makes studying such systems a topic for social science as well as for natural science and engineering. However, the issues transcend disciplinary boundaries and making progress will require a significant interdisciplinary effort.

 

Much of the research that is required to address these issues is taking place at a new interface, where collaboration between economists, demographers, sociologists, etc., is supported and catalysed by tools and concepts from the physical sciences, mathematics, computer science and engineering. In the same way that research at the life and physical sciences interface has revolutionised biology and medicine since the turn of the century, research at the social sciences interface has the potential to transform our ability to answer questions about social, socio-economic, socio-ecological and socio-technological systems.

 

Contributions in the form of papers of 2500 – 8000 words reporting work that straddles the interface between complexity science and social science are invited.  The intention is that a collection of papers will be published after the conference as a special issue of a prestigious journal.  Papers describing applications are especially welcomed.  There will also be an opportunity to present posters.

 

Date: 24th and 25th September 2012

Venue: Chicheley Hall, Royal Society International Centre, Newport Pagnell, UK.

Link venue  http://bit.ly/ieya3m

To Attend: Follow the link to a page with further information and to submit an abstract or expression of interest to attend through the online form. 

Link Conference: http://www.csrw.ac.uk/events-hosted/complex-interface-conference-2012

Deadline: 1st June 2012. Places will be confirmed by 1st August 2012.

Queries to Prof. Nigel Gilbert n.gilbert@surrey.ac.uk and Alison Cooper (network coordinator) Alison.Cooper@surrey.ac.uk

Models are dangerous!

We need to be aware of what we are doing when assigning probabilities - most people somehow seem to tend to forget that:

• they are mistaking the model for reality, and even worse
• they are forgetting about the reality check of the model in suitable intervals
• however, a bad model may be worse than no model at all.

Of course you can assign probabilities to your  guesses about future developments and use scenarios and decisions trees. However, that assumes we know the future states of the world in sufficient detail to make relative or absolute judgement calls about their likelihood.


Now, as Knight, Popper (and based on him Soros), and Taleb argue, the future is open and we cannot know it 'completely' enough to rely on our models.


Walking along river Danube we (may) feel confirmed about our (locally correct) belief in white swans by every observation of another white swan around the next bend - which we turn into a belief on the existence of only white swans - until we meet a black swan from Australia. In this, there is an epistemic problem that we cannot extrapolate from a limited observation / induction base to a 'totality' of states of the world.


We often cannot know the future 'completely' or sufficiently to make the assumptions we are psychologically and institutionally 'required' to make, because people (esp. in large and political organizations, without vision and / or strong leaders) cannot remain confident in the face of having to admit to (brutal epistemic) uncertainty.


Of course we can adapt our (inferred) states of the world and probabilities as we go along and learn, but that requires changing the model on the fly - however, what is your justification for the model if you have to update it frequently (in the large, political, visionless organizations above)?


If we are dealing with 'closed', simple systems that follow (or can be approximated by) linear models a probability approach is fine, we can assign probabilities and measure risks. If things get more complex and complicated we run into the epistemic limits described above and we face uncertainty and cannot assign probabilities anymore - as Knight argued.


Here is alink to Knight's book: Risk, Uncertainty and Profit

Strategy and Finance on Moving Landscapes

When implementing a new strategy, which time horizon do you set and how much negative cash flow are you prepared to accept until the realization of the outcome? 

That is how patient are 'investors' and how deep are their pockets (resp. how good management at selling their story).

In terms of time horizons and deep pockets I like the concept of fitness landscapes (which has probably not been interpreted 'right' in its original biological context), where organizations and strategies have to move through hills and valleys of relative fitness (another concept with some issues), respectively have to move on a landscape that is moving itself.

Nevertheless, as a metaphor with quite some abstract and mathematical apparatus associated it seems useful. For instance, one could argue that non-monetary (that is hard to financially value or associated with immediate financial returns) factors can be brought into such a conceptualization impacting on 'fitness' of an organization or strategy (composed of financial and non-financial factors).

Growing niches for products / services, which can build on their accumulated economics of scale and scope nicely fit into this conceptualization as well.

Complexity, Evolution and Near decomposability

I have been pointing out the relevance of (nearly) decomposable systems, modularity and hierarchy to deal with complex systems before. Some of you had asked for a summary at some point.

I would like to know what you think of the concept and where you deem it useful for our discussions. 

•         It is his story of how variety generation can produce structure (organized) complex results relatively quickly by employing hierarchical relationships in evolution. 

•         It is potentially interesting wrt how costs of interaction between interfaced systems composed of (sub-)systems can be explained and measured

•         It provides a potential basis for the measurement of “complexity” and risk based on the interaction strengths between systems.

Here is a summary of one of the core articles of Simon on the evolution of complex systems based on hierarchy and nearly decomposable system. 

   

It gives a summary of his thinking on this subject inspired by natural systems and the application to social systems. 

His view of the evolution of complex structures is based on the following assumption:

If the existence of a particular complex form increases the probability of the creation of another form just like it, the equilibrium between complexes and components could be greatly altered in favor of the former.

This process (obviously) makes evolution of complex structures from building blocks in hierarchic structures much faster than the independent evolution of complex structures. The result of the process of evolution “employing” such an approach is near-decomposability (again – as it is based on simpler elements that are interacting more strongly internally than externally).

Simon defines near decomposability as follows:

(a) in a nearly decomposable system, the short-run behavior of each of the component subsystems is approximately independent of the short-run behavior of the other components; 

(b) in the long run, the behavior of any one of the components depends in only an aggregate way on the behavior of the other components.

He defines hierarchy as follows:

By a hierarchic system, or hierarchy, I mean a system that is composed of interrelated subsystems, each of the latter being, in turn, hierarchic in structure until we reach some lowest level of elementary subsystem. 

And qualifies the arbitrary notion of the measurement scale of a system wrt measurement / classification of the basic elements (defined as systems) by saying:

In most systems in nature, it is somewhat arbitrary as to where we leave off the partitioning, and what subsystems we take as elementary.

He further qualifies it by comparing to social organizations (which he described before)

Etymologically, the word "hierarchy" has had a narrower meaning than I am giving it here. The term has generally been used to refer to a complex system in which each of the subsystems is subordinated by an authority relation to the system it belongs to. More exactly, in a hierarchic formal organization, each system consists of a "boss" and a set of subordinate subsystems. Each of the subsystems has a "boss" who is the immediate subordinate of the boss of the system. We shall want to consider systems in which the relations among subsystems are more complex than in the formal organizational hierarchy just described. We shall want to include systems in which there is no relation of subordination among subsystems.

which relates to 

NEARLY DECOMPOSABLE SYSTEMS. We can distinguish between the interactions among subsystems, on the one hand, and the interactions within subsystems. ... As a second approximation, we may move to a theory of nearly decomposable systems, in which the interactions among the subsystems are weak, but not negligible.

THE DESCRIPTION OF COMPLEXITY. The fact, then, that many complex systems have a nearly decomposable, hierarchic structure is a major facilitating factor enabling us to understand, to describe, and even to "see" such systems and their parts.

The definition of near decomposability can be mode more formal (where it ordered such that strongly interacting elements are placed near each other):

This article treats of systems that are nearly decomposable--systems with matrices whose elements, except within certain submatrices along the main diagonal, approach zero in the limit. Such a system can be represented as a superposition of (1) a set of independent subsystems ... and (2) an aggregate system having one variable for each subsystem. ...

From the abstract of Simon and Ando's 1961 Aggregation of Variables in Dynamic Systems

which then allows to deal with the analysis and measurement of complex systems:

There is redundancy in complexity which takes a number of forms: Hierarchic systems are usually composed of only a few different kinds of subsystems, in various combinations and arrangements. Hierarchic systems are, as we have seen, often nearly decomposable. 

Hence only aggregative properties of their parts enter into the description of the interactions of those parts. By appropriate "recoding," the redundancy that is present but unobvious in the structure of a complex system can often be made patent. 

This allows us to reduce “the complexity” of the measurement of complex systems by compartmentalizing interactions and effects in areas or “boxes”.

N.B.: If one reads several of Simon's papers on causality and measurement there are a number of Machian perspectives and topics, however Mach's evolutionary / developmental / genetic perspective gets crushed in a systemic classification of the (Simonian) world, where the classification system seems to introduce a static nature. (Please correct me if you think I am wrong!)

I would greatly appreciate your feedback and thank for your attention!

Common evolutionary processes in science and arts

I think there is an interesting topic re arts, engineering and scientific methods, bodies of knowledge and methods of gaining knowledge, which can both be seen as extension of natural evolution to the cultural domain (in line with Ernst Mach, who extended Darwin's principles of evolution to how knowledge is gained very early after Darwin's publication).

Thinking of writing (crafting) a poem or a novel, it does not seem to be too far away from crafting a technical application or a scientific hypothesis, respectively its proof either through experiment or "logical" derivation. - All of these processes build on accumulation of test cases, sketches, drafts, observations, and trial and error wrt putting the dots together, building connections between "input" and "output".

So, I suspect, principles of tinkering, bricolage and learning by doing, resp. by trial and error to derive some integrative "picture" should be similar in engineering, science and in arts. In all areas these are based on intuition, though I further suspect the rationalization of how insights are gained is different: science and engineering emphasize logical rationalizations, while arts emphasize intuitive, creative rationalizations of how results have been achieved ... and tell their stories accordingly.
A post of mine in a LI discussion in the Scientist Artist Collaboration Group

Strategy, Intuition and Analysis

I believe that the strategy toolbox offers a useful set of tools to order and frame thinking about strategic issues. So the tools are limited, if one or a few are taken as "the" approach, especially if used without understanding the tacit knowledge element they can convey. 

One of these elements should be to think differently, outside the usual box - to develop risk scenarios and to envisage new approaches to or domains of business. Otherwise, if every strategist thinks BCG and 5 forces most companies will end up doing roughly the same in better or worse ways. 

Thus, what matters is the teachable but difficult to formalize way of thinking of a strategist, which means that economics, politics, history, philosophy as well as psychology and sociology as well as math, statistics, OR, and natural science (physics, chemistry, biology) can offer relevant models and mind sets to engage in strategy. For instance, I liked history a lot and learned I believe quite something about thinking in developments and scenarios as much as I learned about macro systems and variation from evolutionary theory. 

Knowledge about pertinent business domains is obviously useful - however, only up to the point that it does not blind the strategist - but how do you discover the cut-off point?! - Let's assume it is the mid to end 70s: Is the young guy in front of you talking about ... say computers on every desk and how to place them there a wacky lunatic or visionary business man - and if he is one of the two for you ... how big is the divide between the two? 

For me this points to the importance of the combination of creativity and intuition with formalized research and analysis, the specific tools are secondary, but what they can create in 'prepared mind' matters.

The sum is (not) greater than its parts

An explanatory / historical side-note on "the sum is greater than its parts" - concept:

Its origin seemingly goes back to concepts used by Henry Lewes and J.S. Mill, where a differentiation is made between the simple additive combination of forces (described by vectors) and the qualitatively different properties due to the combination of chemical elements leading to 'emergent' properties of H2O relative to its constituents H and O:


Lewes says: “The emergent is unlike its components in so far as these are incommensurable, and it cannot be reduced either to their sum or their difference”  (Lewes 1875, 413)

(N.B.: consider "or their difference" - it seems half of the story is forgotten, and not reduceable to the sum has been turned into greater than the sum, which adds some factor in my view.)

Mill says: “The chemical combination of two substances produces [...] a third substance with properties different from those of either of the two substances separately, or of both of them taken together . […] There, most of the uniformities, to which the causes conformed when separate, cease altogether when they are conjoined; and we are not, at least in the present state of our knowledge, able to foresee what result will follow from any new combination, until we have tried the specific experiment.” (Mill 1843, 371, bk3, ch6, §1)

The qualifier "present state of knowledge" is important here. Today we can (better) explain why the combination of two gases forms water. This indicates that we like to take as emergent what we cannot (yet) explain.

Thus emergence is the surprising occurence of a phenomenon we cannot currently explain - but may be able to explain in the future. The modern sum is greater than its parts is obviously a qualitative interpretation: water is definitely interesting in its properties, but why should its properties be "more" than that of H and O?

What are epistemological issues wrt the measurement of organizational complexity? Can one measure complexity or not - based on your definition of complexity?

We had some discussions on epistemological questions regarding the scientific process and measurement in general in the context of the measurement of the complexity of organisations (structures, processes). 

Differences in the definition of complexity and worldview regarding what constitutes science resp. how to do science in different disciplines obviously affect the answer - what is your take on this?

There will be value driven, socialization based, work field / discipline related differences in worldview - so please consider this in the tone of the discussion and be open and friendly to these interpretations of the world.A discussion I have started in the Quantitative Complexity Group on Linkedin

Is complexity measurement of organizations posible and feasible?

Organizations can be seen as hierarchical systems with business line / unit and departmental ‘modules’ that allow execution of specific functions through specific capabilities concentrated in particular areas. This confers economies through separation of work but also leads to interpretation and filtering problems in non-standard or changing situations - interpretative blindness and inertia are fostered in organizations. Therefore, organizations need to be heterarchical. Particularly in conditions of increased complexity and speed of change we face today. 

Heterarchical structures allow faster and broader interpretation of information, but also demand higher interpretative capabilities by management. Given traditional and often still normal ‘linearly’ organized procedures and structures, these interpretative capabilities determine to a large extent the success of an organization – as Edith Penrose already highlighted.

On the other hand, functional and departmental decomposability, i.e. separable modularity of an organization correlates with flexibility, adaptability and ease of change of an organization.

Does measurement of decomposability (e.g. based on Simon’s near decomposability) allow for a measurement and thus management of organizational complexity? What do you think?

A discussion I have started in the Quantitave Complexity Management Group on Linkedin: