Archive for the 'Planning' Category

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.




On Getting Things Done

Published
by
peter
on 2011-01-27
in Anthropology, Argumentation, Getting-things-done intelligence, Joint-Action Society, Planning, Project Management and Team working
. 2 Comments

New York Times Op-Ed writer, David Brooks, has two superb articles about the skills needed to be a success in contemporary technological society, the skills I refer to as Getting-Things-Done Intelligence.  One is a short article in The New York Times (2011-01-17), reacting to the common, but wrong-headed, view that technical skill is all you need for success, and the other a long, fictional disquisition in The New Yorker (2011-01-17) on the social skills of successful people.  From the NYT article:

Practicing a piece of music for four hours requires focused attention, but it is nowhere near as cognitively demanding as a sleepover with 14-year-old girls. Managing status rivalries, negotiating group dynamics, understanding social norms, navigating the distinction between self and group — these and other social tests impose cognitive demands that blow away any intense tutoring session or a class at Yale.

Yet mastering these arduous skills is at the very essence of achievement. Most people work in groups. We do this because groups are much more efficient at solving problems than individuals (swimmers are often motivated to have their best times as part of relay teams, not in individual events). Moreover, the performance of a group does not correlate well with the average I.Q. of the group or even with the I.Q.’s of the smartest members.

Researchers at the Massachusetts Institute of Technology and Carnegie Mellon have found that groups have a high collective intelligence when members of a group are good at reading each others’ emotions — when they take turns speaking, when the inputs from each member are managed fluidly, when they detect each others’ inclinations and strengths.

Participating in a well-functioning group is really hard. It requires the ability to trust people outside your kinship circle, read intonations and moods, understand how the psychological pieces each person brings to the room can and cannot fit together.

This skill set is not taught formally, but it is imparted through arduous experiences. These are exactly the kinds of difficult experiences Chua shelters her children from by making them rush home to hit the homework table.”

These articles led me to ask exactly what is involved in reading a social situation?  Brooks mentions some of the relevant aspects, but not all.   To be effective, a manager needs to parse the social situation of the groups he or she must work with – those under, those over and peer groups to the side – to answer questions such as the following:

  • Who has power or influence over each group?  Is this exercised formally or informally?
  • What are the norms and practices of the group, both explicit and implicit, known and unconscious?
  • Who in the group is reliable as a witness?   Whose stories can be believed?
  • Who has agendas and what are these?
  • Who in the group is competent or capable or intelligent?  Whose promises to act can be relied upon?  Who, in contrast, needs to be monitored or managed closely?
  • What constraints does the group or its members operate under?  Can these be removed or side-stepped?
  • What motivates the members of the group?  Can or should these motivations be changed, or enhanced?
  • Who is open to new ideas, to change, to improvements?
  • What obstacles and objections will arise in response to proposals for change?  Who will raise these?  Will these objections be explicit or hidden?
  • Who will resist or oppose change?  In what ways? Who will exercise pocket vetos?

Parsing new social situations – ie, answering these questions in a specific situation – is not something done in a few moments.  It may take years of observation and participation to understand a new group in which one is an outsider.  People who are good at this may be able to parse the key features of a new social landscape within a few weeks or months, depending on the level of access they have, and the willingness of the group members to trust them.     Good management consultants, provided their sponsors are sufficiently senior, can often achieve an understanding within a few weeks.   Experience helps.

Needless to say, most academic research is pretty useless for these types of questions.  Management theory has either embarked on the reduce-and-quantify-and-replicate model of academic psychology, or else undertaken the narrative descriptions of successful organizations of most books by business gurus.   Narrative descriptions of failures would be far more useful.

The best training for being able to answer such questions – apart from experience of life – is the study of anthropology or literature:  Anthropology because it explores the social structures of other cultures and the factors within a single lifetime which influence these structures, and Literature because it explores the motivations and consequences of human actions and interactions.   It is no coincidence, in my view, that the British Empire was created and run by people mostly trained  in Classics, with its twofold combination of the study of alien cultures and literatures, together with the analytical rigor and intellectual discipline acquired through the incremental learning of those difficult subjects, Latin and Ancient Greek languages.

UPDATE (2011-02-16): From Norm Scheiber’s profile of US Treasury Secretary Timothy Geithner in The New Republic (2011-02-10):

“Tim’s real strength … is that he’s really quick at reading the culture of any institutions,” says Leslie Lipschitz, a former Geithner deputy.

The profile also makes evident Geithner’s agonistic planning approach to policy – seeking to incorporate opposition and minority views into both policy formation processes and the resulting policies.




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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Complex Decisions

Published
by
peter
on 2010-06-27
in Decision theory, Economics, Forecasting, Game theory, Joint-Action Society, Market planning, Military strategy, Planning, Project Management and Team working
. Closed

Most real-world business decisions are considerably more complex than the examples presented by academics in decision theory and game theory. What makes some decisions more complex than others? Here I list some features, not all of which are present in all decision situations.

  • The problems are not posed in a form amenable to classical decision theory.

    Decision theory requires the decision-maker to know what are his or her action-options, what are the consequences of these, what are the uncertain events which may influence these consequences, and what are the probabilities of these uncertain events (and to know all these matters in advance of the decision). Yet, for many real-world decisions, this knowledge is either absent, or may only be known in some vague, intuitive, way. The drug thalidomide, for example, was tested thoroughly before it was sold commercially – on male and female human subjects, adults and children. The only group not to be tested were pregnant women, which were, unfortunately, the main group for which the drug had serious side effects. These side effects were consequences which had not been imagined before the decision to launch was made. Decision theory does not tell us how to identify the possible consequences of some decision, so what use is it in real decision-making?

  • There are fundamental domain uncertainties.

    None of us knows the future. Even with considerable investment in market research, future demand for new products may not be known because potential customers themselves do not know with any certainty what their future demand will be. Moreover, in many cases, we don’t know the past either. I have had many experiences where participants in a business venture have disagreed profoundly about the causes of failure, or even success, and so have taken very different lessons from the experience.

  • Decisions may be unique (non-repeated).

    It is hard to draw on past experience when something is being done for the first time. This does not stop people trying, and so decision-making by metaphor or by anecdote is an important feature of real-world decision-making, even though mostly ignored by decision theorists.

  • There may be multiple stakeholders and participants to the decision.

    In developing a business plan for a global satellite network, for example, a decision-maker would need to take account of the views of a handful of competitors, tens of major investors, scores of minor investors, approximately two hundred national and international telecommunications regulators, a similar number of national company law authorities, scores of upstream suppliers (eg equipment manufacturers), hundreds of employees, hundreds of downstream service wholesalers, thousands of downstream retailers, thousands or millions of shareholders (if listed publicly), and millions of potential customers. To ignore or oppose the views of any of these stakeholders could doom the business to failure. As it happens, Game Theory isn’t much use with this number and complexity of participants. Moreover, despite the view commonly held in academia, most large Western corporations operate with a form of democracy. (If opinions of intelligent, capable staff are regularly over-ridden, these staff will simply leave, so competition ensures democracy. In addition, good managers know that decisions unsupported by their staff will often be executed poorly, so success of a decision may depend on the extent to which staff believe it has been reached fairly.) Accordingly, all major decisions are decided by groups or teams, not at the sole discretion of an individual. Decision theorists, it seems to me, have paid insufficient attention to group decisions: We hear lots about Bayesian decision theory, but where, for example, is the Bayesian theory of combining subjective probability assessments?

  • Domain knowledge may be incomplete and distributed across these stakeholders.
  • Beliefs, goals and preferences of the stakeholders may be diverse and conflicting.
  • Beliefs, goals and preferences of stakeholders, the probabilities of events and the consequences of decisions, may be determined endogenously, as part of the decision process itself.

    For instance, economists use the term network goods to refer to a good where one person’s utility depends on the utility of others. A fax machine is an example, since being the sole owner of fax is of little value to a consumer. Thus, a rational consumer would determine his or her preferences for such a good only AFTER learning the preferences of others. In other words, rational preferences are determined only in the course of the decision process, not beforehand.Having considerable experience in marketing, I contend that ALL goods and services have a network-good component. Even so-called commodities, such as natural resources or telecommunications bandwidth, have demand which is subject to fashion and peer pressure. You can’t get fired for buying IBM, was the old saying. And an important function of advertising is to allow potential consumers to infer the likely preferences of other consumers, so that they can then determine their own preferences. If the advertisement appeals to people like me, or people to whom I aspire to be like, then I can infer that those others are likely to prefer the product being advertized, and thus I can determine my own preferences for it. Similarly, if the advertisement appeals to people I don’t aspire to be like, then I can infer that I won’t be subject to peer pressure or fashion trends, and can determine my preferences accordingly.

    This is commonsense to marketers, even if heretical to many economists.

  • The decision-maker may not fully understand what actions are possible until he or she begins to execute.
  • Some actions may change the decision-making landscape, particularly in domains where there are many interacting participants.

    A bold announcement by a company to launch a new product, for example, may induce competitors to follow and so increase (or decrease) the chances of success. For many goods, an ecosystem of critical size may be required for success, and bold initiatives may act to create (or destroy) such ecosystems.

  • Measures of success may be absent, conflicting or vague.
  • The consequences of actions, including their success or failure, may depend on the quality of execution, which in turn may depend on attitudes and actions of people not making the decision.

    Most business strategies are executed by people other than those who developed or decided the strategy. If the people undertaking the execution are not fully committed to the strategy, they generally have many ways to undermine or subvert it. In military domains, the so-called Powell Doctrine, named after former US Secretary of State Colin Powell, says that foreign military actions undertaken by a democracy may only be successful if these actions have majority public support. (I have written on this topic before.)

  • As a corollary of the previous feature, success of an action may require extensive and continuing dialog with relevant stakeholders, before, during and after its execution.

    This is not news to anyone in business.

  • Success may require pre-commitments before a decision is finally taken.

    In the 1990s, many telecommunications companies bid for national telecoms licences in foreign countries. Often, an important criterion used by the Governments awarding these licences was how quickly each potential operator could launch commercial service. To ensure that they could launch service quickly, some bidders resorted to making purchase commitments with suppliers and even installing equipment ahead of knowing the outcome of a bid, and even ahead, in at least one case I know, of deciding whether or not to bid.

  • The consequences of decisions may be slow to realize.

    Satellite mobile communications networks have typically taken ten years from serious inception to launch of service.  The oil industry usually works on 50+ year cycles for major investment projects.  BP is currently suffering the consequence in the Gulf of Mexico of what appears to be a decades-long culture which de-emphasized safety and adequate contingency planning.

  • Decision-makers may influence the consequences of decisions and/or the measures of success.
  • Intelligent participants may model each other in reaching a decision, what I term reflexivity.

    As a consequence, participants are not only reacting to events in their environment, they are anticipating events and the reactions and anticipations of other participants, and acting proactively to these anticipated events and reactions. Traditional decision theory ignores this. Following Nash, traditional game theory has modeled the outcomes of one such reasoning process, but not the processes themselves. Evolutionary game theory may prove useful for modeling these reasoning processes, although assuming a sequence of identical, repeated interactions does not strike me as an immediate way to model a process of reflexivity. This problem still awaits its Nash.

In my experience, classical decision theory and game theory do not handle these features very well; in some cases, indeed, not at all.  I contend that a new theory of complex decisions is necessary to cope with decision domains having these features.

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GTD Intelligence at Kimberly-Clark

Published
by
peter
on 2009-09-14
in Consumer Behaviour, Corporate culture, Decision theory, Getting-things-done intelligence, Market planning, Planning and Team working
. Closed

I started talking recently about getting-things-done (GTD) intelligence.  Grant McCracken, over at This Blog Sits At, has an interview with Paula Rosch, formerly of fmcg company Kimberly-Clark, which illustrates this nicely.

I spent the rest of my K-C career in advanced product development or new business identification, usually as a team leader, and sometimes as what Gifford Pinchot called an “Intrapreneur” – a corporate entrepreneur, driving new products from discovery to basis-for-interest to commercialization.  It’s the nature of many companies to prematurely dismiss ideas that represent what the world might want/need 5, 10 years out and beyond in favor of near-term opportunities – the intrapreneur stays under the radar, using passion, brains, intuition, stealth, any and every other human and material resource available to keep things moving.  It helps to have had some managers that often looked the other way.

Continue reading ‘GTD Intelligence at Kimberly-Clark’

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Bonuses yet again

Published
by
peter
on 2009-09-09
in Africa, Corporate culture, Decision theory, Forecasting, Market planning, Planning and Project Management
. Closed

Alex Goodall, over at A Swift Blow to the Head, has written another angry post about the bonuses paid to financial sector staff. I’ve been in several minds about responding, since my views seem to be decidedly minority ones in our present environment, and because there seems to be so much anger abroad on this topic.  But so much that is written and said, including by intelligent, reasonable people such as Alex, mis-understands the topic, that I feel a response is again needed.  It behooves none of us to make policy on the basis of anger and ignorance.

Continue reading ‘Bonuses yet again’




Shame!

Published
by
peter
on 2009-06-14
in Corporate culture, Planning and Project Management
. Closed

Visiting my local dojo this week, I saw an advert for a Workaholics Anonymous meeting that also takes place there.  They meet fortnightly, on Saturdays from 10 am to 12 noon. What  a pity, since Saturday mornings are my most productive work-times of the week!




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