Archive for the 'Decision theory' Category

Roughshod Riders

Published
by
peter
on 2011-07-16
in Corporate culture, Decision theory, Politics and Team working
. Closed

One annoying feature of the verbal commentariat is their general lack of real-world business experience.  A fine example has just been provided by political blogger Marbury, who derides Gordon Brown for not asserting himself when Prime Minister over his Cabinet Secretary on the matter of an enquiry into voicemail hacking at certain newspapers.

Well, to be fair to Gordon Brown, Marbury has clearly never led an organization and tried to force the people below him to do something they adamantly oppose doing.  No doubt, Brown when PM could have ordered the Cabinet Secretary to implement a public enquiry, but every single person in the chain of command could then have: (a) leaked the CabSec’s advice opposing the instruction, and/or (b) exercised their pocket veto to delay or prevent the enquiry happening, and/or (c) implemented it in a way which backfired upon Brown and the Cabinet. No rational manager tries to execute a policy his own staff vehemently oppose, even when, as appears to be the case here, he knows he has morality, the law, good governance, and the public interest all on his side.




Markets as feedback mechanisms

Published
by
peter
on 2011-06-14
in Decision theory, Economics, Forecasting, Market planning, Marketing strategy, Military strategy and Telecommunications
. 2 Comments

I just posted after hearing a talk by economic journalist Tim Harford at LSE.  At the end of that post, I linked to a critical review of Harford’s latest book,  Adapt – Why Success Always Starts with Failure, by Whimsley.  This review quotes Harford talking about markets as feedback mechanisms:

To identify successful strategies, Harford argues that “we should not try to design a better world. We should make better feedback loops” (140) so that failures can be identified and successes capitalized on. Harford just asserts that “a market provides a short, strong feedback loop” (141), because “If one cafe is ordering a better combination of service, range of food, prices, decor, coffee blend, and so on, then more customers will congregate there than at the cafe next door“, but everyday small-scale examples like this have little to do with markets for credit default swaps or with any other large-scale operation.

Yes, indeed.  The lead-time between undertaking initial business planning in order to raise early capital investments and the launching of services to the  public for  global satellite communications networks is in the order of 10 years (since satellites, satellite networks and user devices need to be designed, manufactured, approved by regulators, deployed, and connected before they can provide service).  The time between initial business planning and the final decommissioning of an international gas or oil pipeline is about 50 years.  The time between initial business planning and the final decommissioning of an international undersea telecommunications cable may be as long as 100 years.   As I remarked once previously, the design of Transmission Control Protocol (TCP) packets, the primary engine of communication in the 21st century Internet, is closely modeled on the design of telegrams first sent in the middle of the 19th century.  Some markets, if they work at all, only work over the long run, but as Keynes famously said, in the long run we are all dead.

I have experience of trying to design telecoms services for satellite networks (among others), knowing that any accurate feedback for design decisions may come late or not at all, and when it comes may be vague and ambiguous, or even misleading.   Moreover, the success or failure of the selected marketing strategy may not ever be clear, since its success may depend on the quality of execution of the strategy, so that it may be impossible to determine what precisely led to the outcome.   I have talked about this issue before, both regarding military strategies and regarding complex decisions in general.  If the quality of execution also influences success (as it does), then just who or what is the market giving feedback to?

In other words, these coffees are not always short and strong (in Harford’s words), but may be cold, weak, very very slow in arriving, and even their very nature contested.   I’ve not yet read Harford’s book, but if he thinks all business is as simple as providing fmc (fast-moving consumer) services, his book is not worth reading.

Once again, an economist argues by anecdote and example.  And once again, I wonder at the world:  That economists have a reputation for talking about reality, when most of them evidently know so little about it, or reduce its messy complexities to homilies based on the operation of suburban coffee shops.




Tim Harford at LSE: Dirigisme in action

Published
by
peter
on 2011-06-11
in Decision theory, Economics, Forecasting, Global Economic Crisis and Project Management
. 2 Comments

This week  I heard economic journalist Tim Harford talk at the London School of Economics (LSE), on a whirlwind tour (7 talks, I think he told us, this week) to promote his new book.   Each talk is on one topic covered in the book, and at LSE he talked about the GFC and his suggestions for preventing its recurrence.

Harford’s talk itself was chatty, anecdotal, and witty.    Economics is still in deep thrall to its 19th century fascination with physical machines, and this talk was no exception.   The anecdotes mostly concerned Great Engineering Disasters of our time, with Harford emphasizing the risks that arise from tightly-coupling of components in systems and, ironically, frequent misguided attempts to improve their safety which only worsen it.

Anecdotal descriptions of failed engineering artefacts may have relevance to the preventing a repeat of the GFC, but Harford did not make any case that they do.  He just gave examples from engineering and from financial markets, and asserted that these were examples of the same conceptual phenomena.    However, as metaphors for economies machines and mechanical systems are worse than useless, since they emphasize in people’s minds, especially in the minds of regulators and participants, mechanical and stand-alone aspects of systems which are completely inappropriate here.   Economies and marketplaces are NOT like machines, with inanimate parts whose relationships are static and that move when levers are pulled, or effects which can be known or predicted when causes are instantiated, or components designed centrally to achieve some global objectives.  Autonomous, intelligent components having dynamic relationships describes few machines or mechanical systems, and certainly none from the 19th century.   

A better category of failure metaphors would be ecological and biological.   We introduce cane toads to North Queensland to prey upon a sugar cane pest, and the cane toads, having no predators themselves,  take over the country.    Unintended and unforeseen consequences of actions, not arising merely because the  system is complex or its parts tightly-coupled, but arise because the system comprises multiple autonomous and goal-directed actors with different beliefs, histories and motivations, and whose relationships with one another change as a result of their interactions.  

Where, I wanted to shout to Harford, were the ecological metaphors?  Why, I wanted to ask, does this 19th-century fascination with deterministic, centralized machines and mechanisms persist in economics, despite its obvious irrelevance and failings? Who, if not rich FT journalists with time to write books, I wanted to know, will think differently about these problems?

Finally, only economists strongly in favour of allowing market forces to operate unfettered would have used the dirigismic methods that the LSE did to allocate people to seats for this lecture.  We were forced to sit in rows in our order of arrival in the auditorium. Why was this?  When I asked an usher for the reason, the answer I was given made no sense:   Because we expect a full hall.    Why were the organizers so afraid of allowing people to exercise their own preferences as to where to sit?  We don’t all have the same hearing and sight capabilities, we don’t all have the same preferences as to side of the hall, or side  of the aisle, etc. We don’t all arrive in parties of the same size.  We don’t all want to sit behind a tall person or near a noisy group.

The hall was not full, as it happened, so we were crammed into place in part of the hall like passive objects in a consumer choice model of voting, instead of as free, active citizens in a democracy occupying whatever position we most preferred of those still available.  But even if the hall had been full, there are less-centralized and less-unfriendly methods of matching people to seats.  The 20 or so LSE student ushers on hand, for instance, could been scattered about the hall to direct latecomers to empty seats, rather than lining the aisles like red-shirted troops to prevent people sitting where they wanted to.

What hope is there that our economic problems will be solved when the London School of Economics, of all places, uses central planning to sit people in public lectures?

Update: There is an interesting critical review of Harford’s latest book, here.




What use are models?

Published
by
peter
on 2011-04-27
in Argumentation, Computer Science, Computing-as-interaction, Decision theory, Economics, Forecasting, Joint-Action Society, Marketing strategy, Mathematics, Military strategy, Planning, Prophecy, Team working and Uncertainty
. Closed

What are models for?   Most developers and users of models, in my experience, seem to assume the answer to this question is obvious and thus never raise it.   In fact, modeling has many potential purposes, and some of these conflict with one another.   Some of the criticisms made of particular models arise from mis-understandings or mis-perceptions of the purposes of those models, and the modeling activities which led to them.

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.




Dialogs over actions

Published
by
peter
on 2010-12-24
in Argumentation, Computer Science, Computing-as-interaction, Decision theory and Philosophy of Language
. Closed

In the post below, I mentioned the challenge for knowledge engineers of representing know-how, a task which may require explicit representation of actions, and sometimes also of utterances over actions.  The know-how involved in steering a large sailing ship with its diverse crew surely includes the knowledge of who to ask (or to command) to do what, when, and how to respond when these requests (or commands) are ignored, or fail to be executed successfully or timeously.

One might imagine epistemology – the philosophy of knowledge – would be of help here.  Philosophers, however, have been seduced since Aristotle with propositions (factual statements about the world having truth values), largely ignoring actions, and their representation.   Philosophers of language have also mostly focused on speech acts – utterances which act to change the world – rather than on utterances about actions themselves.  Even among speech act theorists the obsession with propositions is strong, with attempts to analyze utterances which are demonstrably not propositions (eg, commands) by means of implicit assertive statements – propositions  asserting something about the world, where “the world” is extended to include internal mental states and intangible social relations between people – which these utterances allegedly imply.  With only a few exceptions (Thomas Reid 1788, Adolf Reinach 1913, Juergen Habermas 1981, Charles Hamblin 1987), philosophers of language have mostly ignored utterances  about actions.

Consider the following two statements:

I promise you to wash the car.

I command you to wash the car.

The two statements have almost identical English syntax.   Yet their meanings, and the intentions of their speakers, are very distinct.  For a start, the action of washing the car would be done by different people – the speaker and the hearer, respectively (assuming for the moment that the command is validly issued, and accepted).  Similarly, the power to retract or revoke the action of washing the car rests with different people – with the hearer (as the recipient of the promise) and the speaker (as the commander), respectively.

Linguists generally use “semantics” to refer to the real-world referants of syntactically-correct expressions, while “pragmatics” refers to other aspects of the meaning and use of an expression not related to their relationship (or not) to things in the world, such as the speaker’s intentions.  For neither of these two expressions does it make sense to speak of  their truth value:  a promise may be questioned as to its sincerity, or its feasibility, or its appropriateness, etc, but not its truth or falsity;  likewise, a command  may be questioned as to its legal validity, or its feasibility, or its morality, etc, but also not its truth or falsity.

For utterances about actions, such as promises, requests, entreaties and commands, truth-value semantics makes no sense.  Instead, we generally need to consider two pragmatic aspects.  The first is uptake, the acceptance of the utterance by the hearer (an aspect first identified by Reid and by Reinach), an acceptance which generally creates a social commitment to execute the action described in the utterance by one or other party to the conversation (speaker or hearer).    Once uptaken, a second pragmatic aspect comes into play:  the power to revoke or retract the social commitment to execute the action.  This revocation power does not necessarily lie with the original speaker; only the recipient of a promise may cancel it, for example, and not the original promiser.  The revocation power also does not necessarily lie with the uptaker, as commands readily indicate.

Why would a computer scientist be interested in such humanistic arcana?  The more tasks we delegate to intelligent machines, the more they need to co-ordinate actions with others of like kind.  Such co-ordination requires conversations comprising utterances over actions, and, for success, these require agreed syntax, semantics and pragmatics.  To give just one example:  the use of intelligent devices by soldiers have made the modern battlefield a place of overwhelming information collection, analysis and communication.  Lots of this communication can be done by intelligent software agents, which is why the US military, inter alia, sponsors research applying the philosophy of language and the  philosophy of argumentation to machine communications.

Meanwhile, the philistine British Government intends to cease funding tertiary education in the arts and the humanities.   Even utilitarians should object to this.

References:

Juergen  Habermas [1984/1981]:   The Theory of Communicative Action:  Volume 1:  Reason and the Rationalization of Society.  London, UK:  Heinemann.   (Translation by T. McCarthy of:  Theorie des Kommunikativen Handelns, Band I,  Handlungsrationalitat und gesellschaftliche Rationalisierung. Suhrkamp, Frankfurt, Germany, 1981.)

Charles  L. Hamblin [1987]:  Imperatives. Oxford, UK:  Basil Blackwell.

P. McBurney and S. Parsons [2007]: Retraction and revocation in agent deliberation dialogs. Argumentation, 21 (3): 269-289.

Adolph Reinach [1913]:  Die apriorischen Grundlagen des bürgerlichen Rechtes.  Jahrbuch für Philosophie und phänomenologische Forschung, 1: 685-847.




Coupling preferences and decision-processes

Published
by
peter
on 2010-11-28
in Decision theory, Economics and Joint-Action Society
. Closed

I have expressed my strong and long-held criticisms of classical decision theory – that based on maximum expected utility (MEU) -  before and again before that.  I want to expand here on one of my criticisms.

One feature of MEU theory is that the preferences of a decision-maker are decoupled from the decision-making process itself.  The MEU process works independently of the preferences of the decision-maker, which are assumed to be independent inputs to the decision-making process.    This may be fine for some decisions, and for some decision-makers, but there are many, many real-world decisions where this decoupling is infeasible or undesirable, or both.

For example, I have talked before about network goods, goods for which the utility received by one consumer depends on the utility received by other consumers.   A fax machine, in the paradigm example, provides no benefits at all to someone whose network of contacts or colleagues includes no one else with a fax machine.   A rational consumer (rational in the narrow sense of MEU theory, as well as rational in the prior sense of being reason-based) would wait to see whether other consumers  in her network decide to purchase such a good (or are likely to decide to purchase it) before deciding to do so herself.   In this case, her preferences are endogeneous to the decision-process, and it makes no sense to model preferences as logically or chronologically prior to the process.   Like most people  in marketing, I have yet to encounter a good or service which is not a network good:  even so-called commodities, like coal, are subject to fashion, to peer-group pressures, and to imitative purchase behaviors.  (In so far as something looks like a commodity in the real world, some marketing manager is not doing his or her job.)

A second class of decisions also require us to consider preferences and decision-processes as strongly coupled.  These are situations where there are multiple decision-makers or stakeholders.     A truly self-interested agent (such as those assumed by mainstream micro-economics) cares not a jot for the interests of other stakeholders, but for those of us out here in the real world, this is almost never the case.  In any multiple-stakeholder decision – ie, any decision where the consequences accrue to more than one party – a non-selfish decision-maker would first seek to learn of the consequences of the different decision-options to other stakeholders as well as to herself, and of the preferences of those other stakeholders over these consequences.  Thus, any sensible decision-making process needs to allow for the elicitation and sharing of consequences and preferences between stakeholders.  In any reasonably complex decision – such as deciding whether to restrict use of some chemical on public health grounds, or deciding on a new distribution strategy for a commercial product  – these consequences will be dispersed and non-uniform in their effects.   This is why democratic government regulatory agencies, such as environmental agencies, conduct public hearings, enquiries and consultations exercises prior to making determinations.  And this is why even the most self-interested of corporate decision-makers invariably consider the views of shareholders, of regulators, of funders, of customers, of supply chain partners (both upstream and downstream), or of those internal staff who will be carrying out the decision, when they want the selected decision-option to be executed successfully.    No CEO is an island.

The fact that the consequences of major regulatory and corporate decisions are usually non-uniform in their impacts on stakeholders  – each decision-option advantaging some people or groups, while disadvantaging others – makes the application of any standard, context-independent decision-rule nonsensical.   Applying standard statistical tests as decision rules falls into this nonsensical category, something statisticians have known all along, but others seem not to. (See the references below for more on this.)

Any rational, feasible decision-process intended for the sorts of decisions we citizens, consumers and businesses face every day needs to allow preferences to emerge as part of the decision-making process, with preferences and the decision-process strongly coupled together.  Once again, as on so many other aspects, MEU theory fails.   Remind me again why it stays in Economics text books and MBA curricula.

References:

L. Atkins and D. Jarrett [1979]:  The significance of “significance tests”.  In:  J. Irvine, I. Miles and J. Evans (Editors): Demystifying Social Statistics. London, UK: Pluto Press.

D. J. Fiorino [1989]:  Environmental risk and democratic process:  a critical review.  Columbia Journal of Environmental Law,  14: 501-547.  (This paper presents reasons why deliberative democratic processes are necessary in environmental regulation.)

T. Page [1978]:  A generic view of toxic chemicals and similar risks.  Ecology Law Quarterly.  7 (2): 207-244.




Distributed cognition

Published
by
peter
on 2010-11-08
in Decision theory, Forecasting, Getting-things-done intelligence, Human intelligence, Joint-Action Society, Planning and Team working
. Closed

Some excerpts from an ethnographic study of the operations of a Wall Street financial trading firm, bearing on distributed cognition and joint-action planning:

This emphasis on cooperative interaction underscores that the cognitive tasks of the arbitrage trader are not those of some isolated contemplative, pondering mathematical equations and connected only to to a screen-world.  Cognition at International Securities is a distributed cognition.  The formulas of new trading patterns are formulated in association with other traders.  Truly innovative ideas, as one senior trader observed, are slowly developed through successions of discreet one-to-one conversations.

. . .

An idea is given form by trying it out, testing it on others, talking about it with the “math guys,” who, significantly, are not kept apart (as in some other trading rooms),  and discussing its technical intricacies with the programmers (also immediately present).”   (p. 265)

The trading room thus shows a particular instance of Castell’s paradox:  As more information flows through networked connectivity, the more important become the kinds of interactions grounded in a physical locale. New information technologies, Castells (2000) argues, create the possibility for social interaction without physical contiguity.  The downside is that such interactions can become repititive and programmed in advance.  Given this change, Castells argues that as distanced, purposeful, machine-like interactions multiply, the value of less-directd, spontaneous, and unexpected interactions that take place in physical contiguity will become greater (see also Thrift 1994; Brown and Duguid 2000; Grabhar 2002).  Thus, for example, as surgical techniques develop together with telecommunications technology, the surgeons who are intervening remotely on patients in distant locations are disproportionately clustering in two or three neighbourhoods of Manhattan where they can socialize with each other and learn about new techniques, etc.” (p. 266)

“One examplary passage from our field notes finds a senior trader formulating an arbitrageur’s version of Castell’s paradox:

“It’s hard to say what percentage of time people spend on the phone vs. talking to others in the room.   But I can tell you the more electronic the market goes, the more time people spend communicating with others inside the room.”  (p. 267)

Of the four statistical arbitrage robots, a senior trader observed:

“We don’t encourage the four traders in statistical arb to talk to each other.  They sit apart in the room.  The reason is that we have to keep diversity.  We could really hammered if the different robots would have the same P&L [profit and loss] patterns and the same risk profiles.”  (p. 283)

References:

Daniel Beunza and David Stark [2008]:  Tools of the trade:  the socio-technology of arbitrage in a Wall Street trading room.  In:  Trevor Pinch and Richard Swedborg (Editors):  Living in a Material World:  Economic Sociology Meets Science and Technology Studies. Cambridge, MA, USA: MIT Press.  Chapter 8, pp. 253-290.

M. Castells [1996]:  The Information Age:  Economy, Society and Culture. Blackwell, Second Edition.




Good decisions

Published
by
peter
on 2010-11-07
in Argumentation, Decision theory, Economics and Planning
. Closed

Which decisions are good decisions?

Since 1945, mainstream economists have arrogated the word “rational” to describe a mode of decision-making which they consider to be best.   This method, called maximum-expected utility (MEU) decision-making, assumes that the decision-maker has only a finite set of possible action-options and that she knows what these are, that she knows the possible consequences of each of these actions and can quantify (or at least can estimate) these consequences, and can do so on a single, common, numerical scale of value (the payoffs), that she knows a finite and complete collection of uncertain events that are possible and which may impact the consequences and their values, and knows (or at least can estimate) the probabilities of these uncertain events, again on a common numerical scale of uncertainty.  The MEU decision procedure is then to quantify the consequences of each action-option, weighting them by the relative likelihood of their arising according to their probabilities of the uncertain events which influence them.

The decision-maker then selects that action-option which has the maximum expected consequential value, ie the consequential value weighted by the probabilities of the uncertain events. Such decision-making, in an abuse of language that cries out for a criminal charges, is then called rational by economists.   Bayesian statistician Dennis Lindley even wrote a book about MEU which included the stunningly-arrogant sentence, “The main conclusion [of this book] is that there is essentially only one way to reach a decision sensibly.”

Rational?  This method is not even feasible, let alone sensible or good!

First, where do all these numbers come from?  With the explicit assumptions that I have listed, economists are assuming that the decision-maker has some form of perfect knowledge.  Well, no one making any real-world decisions has that much knowledge.  Of course, economists often respond, estimates can be used when the knowledge is missing.  But whose estimates?   Sourced from where?   Updated when? Anyone with any corporate or public policy experience knows straight away that consensus on such numbers for any half-way important problem will be hard to find.  Worse than that, any consensus achieved should immediately be suspected and interrogated, since it may be evidence of groupthink.    There simply is no certainty about the future, and if a group of people all do agree on what it holds, down to quantified probabilities and payoffs, they deserve the comeuppance they are likely to get!

Second, the MEU principle simply averages across uncertain events.   What of action-options with potentially catastrophic outcomes?   Their small likelihood of occurrence may mean they disappear in the averaging process, but no real-world decision-maker – at least, none with any experience or common sense – would risk a catastrophic outcome, despite their estimated low probabilities.   Wall Street trading firms have off-street (and often off-city) backup IT systems, and sometimes even entire backup trading floors, ready for those rare events.

Third, look at all the assumptions not made explicit in this framework.  There is no mention of the time allowed for the decision, so apparently the decision-maker has infinities of time available.  No mention is made of the processing or memory resources available for making the decision, so she has infinities of world also.   That makes a change from most real-world decisions:  what a pleasant utopia this MEU-land must be.  Nothing is said – at least nothing explicit – about taking into account the historical or other contexts of the decision, such as past decisions by this or related decision-makers, technology standards, legacy systems, organization policies and constraints, or the strategies of the company or the society in which the decision-maker sits.   How could a decision procedure which ignores such issues be considered, even for a moment, rational?   I think only an academic could ignore context in this way; no business person I know would do so, since certain unemployment would be the result.  And how could members of an academic discipline purporting to be a social science accept and disseminate a decision-making framework which ignores such social, contextual features?

And do the selected action-options just execute themselves?  Nothing is said in this framework about consultation with stakeholders during the decision-process, so presumably the decision-maker has no one to report to, no board members or stockholders or division presidents or ward chairmen or electors to manage or inform or liaise with or mollify or reward or appease or seek re-election from, no technical departments to seek feasibility approval from, no implementation staff to motivate or inspire, no regulators or ethicists or corporate counsel to seek legal approval from, no funders or investors to raise finance from, no suppliers to convince to accept orders with, no distribution channels to persuade to schedule throughput with,  no competitors to second-guess or outwit, and no actual, self-immolating protesters outside one’s office window to avert one’s eyes from and feel guilt about for years afterward.*

For many complex decisions, the ultimate success or failure of the decision can depend significantly on the degree to which those having to execute the decision also support it.  Consequently, the choice of a specific action-option (and the logical reasoning process used to select it) may be far less important for success of the decision than that key stakeholders feel that they have been consulted appropriately during the reasoning process.  In other words, the quality of the decision may depend much more on how and with who the decision-maker reasons than on the particular conclusion she reaches.   Arguably this is true of almost all significant corporate strategy decisions and major public policy decisions:  There is ultimately no point sending your military to prop up an anti-communist regime in South-East Asia, for example, if your own soldiers come to feel they should not be there (as I discuss here, regarding another decision to go to war).

Mainstream economists have a long way to go before they will have a theory of good decision-making.   In the meantime, it would behoove them to show some humility when criticizing the decision-making processes of human beings.**

Notes and Bibliography:

Oskar Lange [1945-46]:  The scope and method of economics.  The Review of Economic Studies, 13 (1): 19-32.

Dennis Lindley [1985]:  Making Decisions.  Second Edition. London, UK: John Wiley and Sons.

L James Savage [1950]: The Foundations of Statistics.  New York, NY, USA:  Wiley.

* I’m sure Robert McNamara, statistician and decision-theory whizz kid, never considered the reactions of self-immolating protesters when making decisions early in his career, but having seen one outside his office window late in his time as Secretary of Defense he seems to have done so subsequently.

** Three-toed sloth comments dialogically and amusingly on MEU theory here.




In defence of futures thinking

Published
by
peter
on 2010-08-18
in Argumentation, Computer Science, Decision theory, Forecasting, Marketing strategy, Planning, Prophecy, Religion and Uncertainty
. Closed

Norm at Normblog has a post defending theology as a legitimate area of academic inquiry, after an attack on theology by Oliver Kamm.  (Since OK’s post is behind a paywall, I have not read it, so my comments here may be awry with respect to that post.)  Norm argues, very correctly, that it is legitimate for theology, considered as a branch of philosophy to, inter alia, reflect on the properties of entities whose existence has not yet been proven.  In strong support of Norm, let me add:  Not just in philosophy!

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.




Agonistic planning

Published
by
peter
on 2010-08-09
in Argumentation, Decision theory, History, Joint-Action Society, Marketing strategy, Military strategy, Planning, Politics and Team working
. Closed

One key feature of the Kennedy and Johnson administrations identified by David Halberstam in his superb account of the development of  US policy on Vietnam, The Best and the Brightest, was groupthink:  the failure of White House national security, foreign policy and defense staff to propose or even countenance alternatives to the prevailing views on Vietnam, especially when these alternatives were in radical conflict with the prevailing wisdom.   Among the junior staffers working in those administrations was Richard Holbrooke, now the US Special Representative for Afghanistan and Pakistan in the Obama administration.  A New Yorker profile of Holbrooke last year included this statement by him, about the need for policy planning processes to incorporate agonism:

“You have to test your hypothesis against other theories,” Holbrooke said. “Certainty in the face of complex situations is very dangerous.” During Vietnam, he had seen officials such as McGeorge Bundy, Kennedy’s and Johnson’s national-security adviser, “cut people to ribbons because the views they were getting weren’t acceptable.” Washington promotes tactical brilliance framed by strategic conformity—the facility to outmaneuver one’s counterpart in a discussion, without questioning fundamental assumptions. A more farsighted wisdom is often unwelcome. In 1975, with Bundy in mind, Holbrooke published an essay in Harper’s in which he wrote, “The smartest man in the room is not always right.” That was one of the lessons of Vietnam. Holbrooke described his method to me as “a form of democratic centralism, where you want open airing of views and opinions and suggestions upward, but once the policy’s decided you want rigorous, disciplined implementation of it. And very often in the government the exact opposite happens. People sit in a room, they don’t air their real differences, a false and sloppy consensus papers over those underlying differences, and they go back to their offices and continue to work at cross-purposes, even actively undermining each other.”  (page 47)
Of course, Holbrooke’s positing of policy development as distinct from policy implementation is itself a dangerous simplification of the reality for most complex policy, both private and public, where the relationship between the two is usually far messier.    The details of policy, for example, are often only decided, or even able to be decided, at implementation-time, not at policy design-time.    Do you sell your new hi-tech product via retail outlets, for instance?  The answer may depend on whether there are outlets available to collaborate with you (not tied to competitors) and technically capable of selling it, and these facts may not be known until you approach them.   Moreover, if the stakeholders implementing (or constraining implementation) of a policy need to believe they have been adequately consulted in policy development for the policy to be executed effectively (as is the case with major military strategies in democracies, for example here), then a further complication to this reductive distinction exists.
 
 
UPDATE (2011-07-03):
British MP Rory Stewart recounts another instance of Holbrooke’s agonist approach to policy in this post-mortem tribute: Holbrooke, although disagreeing with Stewart on policy toward Afghanistan, insisted that Stewart present his case directly to US Secretary of State Hilary Clinton in a meeting that Holbrooke arranged.
 
References:

David Halberstam [1972]:  The Best and the Brightest.  New York, NY, USA: Random House.

George Packer [2009]:  The last mission: Richard Holbrooke’s plan to avoid the mistakes of Vietnam in Afghanistan.  The New Yorker, 2009-09-28, pp. 38-55.

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