Given our inevitably incomplete knowledge about key structural aspects of an ever-changing economy and the sometimes asymmetric costs or benefits of particular outcomes, a central bank needs to consider not only the most likely future path for the economy but also the distribution of possible outcomes about that path. The decisionmakers then need to reach a judgment about the probabilities, costs, and benefits of the various possible outcomes under alternative choices for policy.”

The product of a low-probability event and a potentially severe outcome was judged a more serious threat to economic performance than the higher inflation that might ensue in the more probable scenario.”

In her book, Stasiland: True Stories from Behind the Berlin Wall, Anna Funder writes the following about the opposition to the Socialist Unity Party (SED) in the former German Democratic Republic (the DDR):

I once saw a note on a Stasi file from early 1989 that I would never forget. In it a young lieutenant alerted his superiors to the fact that there were so many informers in chuch opposition groups at demonstrations that they were making these groups appear stronger than they really were. In one of the most beautiful ironies I have ever seen, he dutifully noted that it appeared that, by having swelled the ranks of the opposition, the Stasi was giving the people heart to keep demonstrating against them. (pp. 197-198)

 

NOTE:  A comment about the processes which led to the end of communism in the USSR is contained in this post.

Reference:

Anna Funder [2003]: Stasiland: True Stories from Behind the Berlin Wall. (London, UK: Granta Books).

Liking cladistics as I do, I thought it useful to list all the potential purposes of models and modeling.   The only discussion that considers this topic that I know is a brief discussion by game theorist Ariel Rubinstein in an appendix to a book on modeling rational behaviour (Rubinstein 1998).  Rubinstein considers several alternative purposes for economic modeling, but ignores many others.   My list is as follows (to be expanded and annotated in due course):

• 1. To better understand some real phenomena or existing system.   This is perhaps the most commonly perceived purpose of modeling, in the sciences and the social sciences.
• 2. To predict (some properties of) some real phenomena or existing system.  A model aiming to predict some domain may be successful without aiding our understanding  of the domain at all.  Isaac Newton’s model of the motion of planets, for example, was predictive but not explanatory.   I understand that physicist David Deutsch argues that predictive ability is not an end of scientific modeling but a means, since it is how we assess and compare alternative models of the same phenomena.    This is wrong on both counts:  prediction IS an end of much modeling activity (especially in business strategy and public policy domains), and it not the only means we use to assess models.  Indeed, for many modeling activities, calibration and prediction are problematic, and so predictive capability may not even be  possible as a form of model assessment.
• 3. To manage or control (some properties of) some real phenomena or existing system.
• 4. To better understand a model of some real phenomena or existing system.  Arguably, most of economic theorizing and modeling falls into this category, and Rubinstein’s preferred purpose is this type.   Macro-economic models, if they are calibrated at all, are calibrated against artificial, human-defined, variables such as employment, GDP and inflation, variables which may themselves bear a tenuous and dynamic relationship to any underlying economic reality.   Micro-economic models, if they are calibrated at all, are often calibrated with stylized facts, abstractions and simplifications of reality which economists have come to regard as representative of the domain in question.    In other words, economic models are not not usually calibrated against reality directly, but against other models of reality.  Similarly, large parts of contemporary mathematical physics (such as string theory and brane theory) have no access to any physical phenomena other than via the mathematical model itself:  our only means of apprehension of vibrating strings in inaccessible dimensions beyond the four we live in, for instance, is through the mathematics of string theory.    In this light, it seems nonsense to talk about the effectiveness, reasonable or otherwise, of mathematics in modeling reality, since how we could tell?
• 5. To predict (some properties of) a model of some real phenomena or existing system.
• 6. To better understand, predict or manage some intended (not-yet-existing) artificial system, so to guide its design and development.   Understanding a system that does  not yet exist is qualitatively different to understanding an existing domain or system, because the possibility of calibration is often absent and because the model may act to define the limits and possibilities of subsequent design actions on the artificial system.  The use of speech act theory (a model of natural human language) for the design of artificial machine-to-machine languages, or the use of economic game theory (a mathematical model of a stylized conceptual model of particular micro-economic realities) for the design of online auction sites are examples here.   The modeling activity can even be performative, helping to create the reality it may purport to describe, as in the case of the Black-Scholes model of options pricing.
• 7. To provide a locus for discussion between relevant stakeholders in some business or public policy domain.  Most large-scale business planning models have this purpose within companies, particularly when multiple partners are involved.  Likewise, models of major public policy issues, such as epidemics, have this function.  In many complex domains, such as those in public health, models provide a means to tame and domesticate the complexity of the domain.  This helps stakeholders to jointly consider concepts, data, dynamics, policy options, and assessment of potential consequences of policy options,  all of which may need to be socially constructed. 
• 8. To provide a means for identification, articulation and potentially resolution of trade-offs and their consequences in some business or public policy domain.   This is the case, for example, with models of public health risk assessment of chemicals or new products by environmental protection agencies, and models of epidemics deployed by government health authorities.
• 9. To enable rigorous and justified thinking about the assumptions and their relationships to one another in modeling some domain.   Business planning models usually serve this purpose.   They may be used to inform actions, both to eliminate or mitigate negative consequences and to enhance positive consequences, as in retroflexive decision making.
• 10. To enable a means of assessment of managerial competencies of the people undertaking the modeling activity. Investors in start-ups know that the business plans of the company founders are likely to be out of date very quickly.  The function of such business plans is not to model reality accurately, but to force rigorous thinking about the domain, and to provide a means by which potential investors can challenge the assumptions and thinking of management as way of probing the managerial competence of those managers.    Business planning can thus be seen to be a form of epideictic argument, where arguments are assessed on their form rather than their content, as I have argued here.
• 11. As a means of play, to enable the exercise of human intelligence, ingenuity and creativity, in developing and exploring the properties of models themselves.  This purpose is true of that human activity known as doing pure mathematics, and perhaps of most of that academic activity known as doing mathematical economics.   As I have argued before, mathematical economics is closer to theology than to the modeling undertaken in the natural sciences. I see nothing wrong with this being a purpose of modeling, although it would be nice if academic economists were honest enough to admit that their use of public funds was primarily in pursuit of private pleasures, and any wider social benefits from their modeling activities were incidental.
  

POSTSCRIPT (Added 2011-06-17):  I have just seen Joshua Epstein’s 2008 discussion of the purposes of modeling in science and social science.   Epstein lists 17 reasons to build explicit models (in his words, although I have added the label “0″ to his first reason):

0. Prediction
1. Explain (very different from predict)
2. Guide data collection
3. Illuminate core dynamics
4. Suggest dynamical analogies
5. Discover new questions
6. Promote a scientific habit of mind
7. Bound (bracket) outcomes to plausible ranges
8. Illuminate core uncertainties
9. Offer crisis options in near-real time. [Presumably, Epstein means "crisis-response options" here.]
10. Demonstrate tradeoffe/ suggest efficiencies
11. Challenge the robustness of prevailing theory through peturbations
12. Expose prevailing wisdom as imcompatible with available data
13. Train practitioners
14. Discipline the policy dialog
15. Educate the general public
16. Reveal the apparently simple (complex) to be complex (simple).

These are at a lower level than my list, and I believe some of his items are the consequences of purposes rather than purposes themselves, at least for honest modelers (eg, #11, #12, #16).

References:

Joshua M Epstein [2008]: Why model? Keynote address to the Second World Congress on Social Simulation, George Mason University, USA.  Available here (PDF).

Robert E Marks [2007]:  Validating simulation models: a general framework and four applied examples. Computational Economics, 30 (3): 265-290.

David F Midgley, Robert E Marks and D Kunchamwar [2007]:  The building and assurance of agent-based models: an example and challenge to the field. Journal of Business Research, 60 (8): 884-893.

Robert Rosen [1985]: Anticipatory Systems. Pergamon Press.

Ariel Rubinstein [1998]: Modeling Bounded Rationality. Cambridge, MA, USA: MIT Press.  Zeuthen Lecture Book Series.

Ariel Rubinstein [2006]: Dilemmas of an economic theorist. Econometrica, 74 (4): 865-883.

‘Motive is always difficult to read,’ he replied.  ‘We make a rational assumption about someone’s behaviour based on what we would, or would not, do in the same circumstances, ignoring the otherness of the other. We consider only influences that make us what we are and impose those beliefs on them.  It is the classic mistake of intelligence analysis.’  “

Reference:

Henry Porter [2009]: The Dying Light. London, UK:  Orion Books.

Obscure fact:  Porter (born 1953) is the grand-nephew of novelist Howard Sturgis (1855-1920), step-cousin to George Santayana (1863-1952).

It took until 2006 and the development of x-ray tomography for a plausible theory of the purpose and operations of the Antikythera Mechanism to be proposed (Freeth et al. 2006).   The machine was said to be a physical examplification of  late Greek theories of cosmology, in particular the idea that the motion of a heavenly body could  be modeled by an epicycle – ie, a body traveling around a circle, which is itself moving around some second circle.  This model provided an explanation for the fact that many heavenly bodies appear to move at different speeds at different times of the year, and sometimes even (appear to) move backwards.

There have been two recent developments:  One is the re-creation of the machine (or, rather, an interpretation of it)  using lego components.

The second has arisen from a more careful examination of the details of the mechanism.  According to Marchant (2010), some people now believe that the mechanism examplifies Babylonian, rather than Greek, cosmology.   Babylonian astronomers modeled the movements of heavenly bodies by assuming each body traveled along just one circle, but at two different speeds:  movement in one period of the year being faster than during the other part of the year.

If this second interpretation of the Antikythera Mechanism is correct, then perhaps it was the mechanism itself (or others like it) which gave late Greek astronomers the idea for an epicycle model.   In support of this view is the fact that, apparently, gearing mechanisms and the epicycle model both appeared around the same time, with gears perhaps a little earlier.   So late Greek cosmology (and perhaps late geometry) may have arisen in response to, or at least alongside, practical developments and physical models.   New ideas in computing typically follow the same trajectory – first they exist in real, human-engineered, systems; then, we develop a formal, mathematical theory of them.   Programmable machines, for instance, were invented in the textile industry in the first decade of the 19th century (eg, the Jacquard Loom), but a mathematical theory of programming did not appear until the 1960s.   Likewise, we have had a fully-functioning, scalable, global network enabling multiple, asynchronous, parallel, sequential and interleaved interactions since Arpanet four decades ago, but we still lack a thorough mathematical theory of interaction.

And what have the Babylonians ever done for us?   Apart from giving us our units for measuring of time (divided into 60) and of angles (into 360 degrees)?

References:

T Freeth, Y Bitsakis, X Moussas, JH Seiradaki, A Tselikas, H Mangou, M Zafeiropoulou, R Hadland, D Bate, A Ramsey, M Allen, A Crawley, P Hockley, T Malzbender, D Gelb,W Ambrisco and MG Edmunds [2006]:  Decoding the ancient Greek astronomical calculator known as the Antikythera Mechanism.  Nature,  444 (30):   587-591.  30 November 2006.

J. Marchant [2010]:  Mechanical inspiration.  Nature, 468:  496-498.  25 November 2010.

In business strategy, good decision-making requires consideration of the consequences of potential actions, which in turn requires the consideration of the potential actions of other actors and stakeholders in response to the first set of actions.  These actors may include entities whose existence is not yet known or even suspected, for example, future competitors to a product whose launch creates a new product category.   Why, there’s even a whole branch of strategy analysis, devoted to scenario planning, a discipline that began in the military analysis of alternative post-nuclear worlds, and whose very essence involves the creation of imagined futures (for forecasting and prognosis) and/or imagined pasts (for diagnosis and analysis).   Every good air-crash investigation, medical diagnosis, and police homicide investigation, for instance, involves the creation of imagined alternative pasts, and often the creation of imaginary entities in those imagined pasts, whose fictional attributes we may explore at length.   Arguably, in one widespread view of the philosophy of mathematics, pure mathematicians do nothing but explore the attributes of entities without material existence.

And not just in business, medicine, the military, and the professions.   In computer software engineering, no new software system development is complete without due and rigorous consideration of the likely actions of users or other actors with and on the system, for example.   Users and actors here include those who are the intended target users of the system, as well as malevolent or whimsical or poorly-behaved or bug-ridden others, both human and virtual, not all of whom may even exist when the system is first developed or put into production.      If creative articulation and manipulation of imaginary futures (possible or impossible) is to be outlawed, not only would we have no literary fiction or much poetry, we’d also have few working software systems either.

I have just learnt of the death last  month of Sol Encel, Emeritus Professor of Sociology at the University of New South Wales, and a leading Australian sociologist, scenario planner, and futures thinker.    I took a course on futurology with him two decades ago, and it was one of the most interesting courses I ever studied.  This was  not due to Encel himself, at least not directly, who appeared in human form only at the first lecture.

He told us he was a very busy man, and would certainly not have the time to spare to attend any of the subsequent lectures in the course.  Instead, he had arranged a series of guest lectures for us, on a variety of topics related to futures studies, futurology, and forecasting.  Because he was genuinely important, his professional network was immense and impressive, and so the guest speakers he had invited were a diverse group of prominent people, from different industries, academic disciplines, professions, politics and organizations, each with interesting perspectives or experiences on the topic of futures and prognosis.  The talks they gave were absolutely fascinating.

To accommodate the guest speakers, the lectures were held in the early evening, after normal working hours.  Because of this unusual timing, and because the course assessment comprised only an essay, student attendance at the lectures soon fell sharply.  Often I turned up to find I was the only student present.   These small classes presented superb opportunities to meet and talk with the guest speakers, conversations that usually adjourned to a cafe or a bar nearby.  I learnt a great deal about the subject of forecasting, futures, strategic planning, and prognosis, particularly in real organizations with real stakeholders, from these interactions.  Since he chose these guests, I thus sincerely count Sol Encel as one of the important influences on my thinking about futures.

Here, in a tribute from the Australian Broadcasting Commission, is a radio broadcast Encel made in 1981 about Andrei Sakharov. It is interesting that there appears to have been speculation in the West then has to how the so-called father of the Soviet nuclear bomb could have become a supporter of dissidents.   This question worried, too, the KGB, whose answer was one Vadim Delone, poet.  And here, almost a month after Solomon Encel’s death, is his obituary in the Sydney Morning Herald.  One wonders why this took so long to be published.

Technorati Tags: Sol Encel, futurology

Brian Dillon reviews a British touring exhibition of the art of John Cage, currently at the Baltic Mill Gateshead.

Two quibbles:  First, someone who compare’s Cage’s 4′ 33” to a blank gallery wall hasn’t actually listened to the piece.  If Dillon had compared it to a glass window in the gallery wall allowing a view of the outside of the gallery, then he would have made some sense.  But Cage’s composition is not about silence, or even pure sound, for either of which a blank gallery wall might be an appropriate visual representation.  The composition is about ambient sound, and about what sounds count as music in our culture.

Second, Dillon rightly mentions that the procedures used by Cage for musical composition from 1950 onwards (and later for poetry and visual art) were based on the Taoist I Ching.  But he wrongly describes these procedures as being based on “the philosophy of chance.”     Although widespread, this view is nonsense, accurate neither as to what Cage was doing, nor even as to what he may have thought he was doing.   Anyone subscribing to the Taoist philosophy underlying them understands the I Ching procedures as examplifying and manifesting hidden causal mechanisms, not chance.   The point of the underlying philosophy is that the random-looking events that result from the procedures express something unique, time-dependent, and personal to the specific person invoking the I Ching at the particular time they invoke it. So, to a Taoist, the resulting music or art is not “chance” or “random” or “aleatoric” at all, but profoundly deterministic, being the necessary consequential expression of deep, synchronistic, spiritual forces. I don’t know if Cage was himself a Taoist (I’m not sure that anyone does), but to an adherent of Taoist philosophy Cage’s own beliefs or attitudes are irrelevant to the workings of these forces.  I sense that Cage had sufficient understanding of Taoist and Zen ideas (Zen being the Japanese version of Taoism) to recognize this particular feature:  that to an adherent of the philosophy the beliefs of the invoker of the procedures are irrelevant.

In my experience, the idea that the I Ching is a deterministic process is a hard one for many modern westerners to understand, let alone to accept, so entrenched is the prevailing western view that the material realm is all there is.  This entrenched view is only historically recent in the west:  Isaac Newton, for example, was a believer in the existence of cosmic spiritual forces, and thought he had found the laws which governed their operation.    Obversely, many easterners in my experience have difficulty with notions of uncertainty and chance; if all events are subject to hidden causal forces, the concepts of randomness and of alternative possible futures make no sense.  My experience here includes making presentations and leading discussions on scenario analyses with senior managers of Asian multinationals.  

We are two birds swimming, each circling the pond, warily, neither understanding the other, neither flying away.

References:

Kyle Gann [2010]: No Such Thing as Silence.  John Cage’s 4′ 33”.  New Haven, CT, USA:  Yale University Press. 

James Pritchett [1993]:  The Music of John Cage.  Cambridge, UK:  Cambridge University Press.

Technorati Tags: John Cage, I Ching, Zen, Taoism

If all you are doing is writing a blog or researching a story, perhaps you don’t ever appreciate this fact about work.  But anyone tasked with doing something other than writing knows it.   Seth Goodin thinks that within 10 years TV programs about office work will seem to be “quaint antiques”.  I bet him they will not at all.  Moreover, I bet the people in those offices will still be using paper, still having meetings, and still talking by the water-cooler.   In fact, while you’re placing my bets, put me down for 100 years, not 10.

Don’t you ask, Leuconoe – the gods do not wish it to be known -
what end they have given me or to you, and don’t meddle with
Babylonian horoscopes. How much better to accept whatever comes,
whether Jupiter gives us other winters or whether this is our last

now wearying out the Tyrrhenian sea on the pumice stones
opposing it. Be wise, strain the wine and cut back long hope
into a small space. Even as we speak, envious time
flies past. Harvest the day and leave as little as possible for tomorrow.

Reference:

Horace [1997 AD/23 BCE]: The Complete Odes and Epodes. Translation by David West. Oxford, UK: Oxford University Press.

Technorati Tags: Horace, carpe diem