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The macroeconomic dark ages

Published
by
peter
on 2011-09-10
in Economics and Global Economic Crisis
. Closed

Paul Krugman, writing about the failings of macro-economists before and after the Great Recession, notes the wide social consequences of the pro-abstraction, anti-history turn in the study of economics this last half-century.   Sadly, this turn has been another instance of the dominance of Descartian autism in western intellectual culture.

Early in 2009, when the Obama stimulus was under discussion, I was stunned to read statements from a number of well-regarded economists asserting not merely that the plan was a bad idea in practice — a defensible idea — but that debt-financed government spending could not, in principle, raise overall spending. Here’s John Cochrane:

Continue reading ‘The macroeconomic dark ages’




Networks of Banks

Published
by
peter
on 2011-08-09
in Computer Science and Economics
. Closed

The first plenary speaker at the 13th International Conference on E-Commerce (ICEC 2011) in Liverpool last week was Robert, Lord May, Professor of Ecology at Oxford University, former Chief UK Government Scientific Advisor, and former President of the Royal Society.  His talk was part of the special session on Robustness and Reliability of Electronic Marketplaces (RREM 2011), and it was insightful, provocative and amusing.

May began life as an applied mathematician and theoretical physicist (in the Sydney University of Harry Messel), then applied his models to food webs in ecology, and now finds the same types of network and lattice models useful for understanding inter-dependencies in networks of banks.  Although, as he said in his talk, these models are very simplified, to the point of being toy models, they still have the power to demonstrate unexpected outcomes:  For example, that actions which are individually rational may not be desirable from the perspective of a system containing those individuals.  (It is one of the profound differences between Computer Science and Economics, that such an outcome would be unlikely to be surprising to most computer scientists, yet seems to be so to mainstream Economists, imbued with a belief in metaphysical carpal entities.)

From the final section of Haldane and May (2011):

The analytic model outlined earlier demonstrates that the topology of the financial sector’s balance sheet has fundamental implications for the state and dynamics of systemic risk. From a public policy perspective, two topological features are key.

First, diversity across the financial system. In the run-up to the crisis, and in the pursuit of diversification, banks’ balance sheets and risk management systems became increasingly homogenous. For example, banks became increasingly reliant on wholesale funding on the liabilities side of the balance sheet; in structured credit on the assets side of their balance sheet; andmanaged the resulting risks using the same value-at-risk models. This desire for diversificationwas individually rational from a risk perspective. But it came at the expense of lower diversity across the system as whole, thereby increasing systemic risk.Homogeneity bred fragility (N. Beale and colleagues, manuscript in preparation).

In regulating the financial system, little effort has as yet been put into assessing the system-wide characteristics of the network, such as the diversity of its aggregate balance sheet and risk management models. Even less effort has been put into providing regulatory incentives to promote diversity of balance sheet structures, business models and risk management systems. In rebuilding and maintaining the financial system, this systemic diversity objective should probably be given much greater prominence by the regulatory community.

Second, modularity within the financial system. The structure of many non-financial networks is explicitly and intentionally modular.  This includes the design of personal computers and the world wide web and the management of forests and utility grids. Modular configurations prevent contagion infecting the whole network in the event of nodal failure. By limiting the potential for cascades, modularity protects the systemic resilience of both natural and constructed networks.

The same principles apply in banking. That is why there is an ongoing debate on the merits of splitting banks, either to limit their size (to curtail the strength of cascades following failure) or to limit their activities (to curtail the potential for cross-contamination within firms). The recently proposed Volcker rule in the United States, quarantining risky hedge fund, private equity and proprietary trading activity from other areas of banking business, is one example of modularity in practice. In the United Kingdom, the new government have recently set up a Royal Commission to investigate the case for encouraging modularity and diversity in banking ecosystems, as a means of buttressing systemic resilience.

It took a generation for ecological models to adapt. The same is likely to be true of banking and finance.”

It would be interesting to consider network models which are more realistic than these toy versions, for instance, with nodes representing banks with goals, preferences and beliefs.

 

References:

F. Caccioli, M. Marsili and P. Vivo [2009]: Eroding market stability by proliferation of financial instruments. The European Physical Journal B, 71: 467–479.

Andrew Haldane and Robert May [2011]: Systemic risk in banking ecosystems. Nature, 469:  351-355.

Robert May, Simon Levin and George Sugihara [2008]: Complex systems: ecology for bankers. Nature, 451, 893–895.

Also, the UK Government’s 2011 Foresight Programme on the Future of Computer Trading in Financial Markets has published its background and working papers, here.

 




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.




ABS Cadets 1979

Published
by
peter
on 2011-01-05
in Computer Science, Economics, Mathematics and Matherati
. Closed

Australian Bureau of Statistics graduate cadets, 1979:

  • Phil Aungles
  • Gail Bansemer
  • Penny Barlow
  • Warren Bird
  • Wendy Darr
  • Ken Henry
  • Karen Hyams
  • Debra Keillor
  • Peter McBurney
  • Vivienne Palmer
  • Prue Phillips
  • Suzanne Sheridan
  • Steven Skates
  • John Stroud.



Concat: The crisis in macroeconomic policy execution

Published
by
peter
on 2010-11-30
in Concats, Economics, Global Economic Crisis and Globalization
. Closed

During the Great Depression, as the Bank of England and British banks were attempting to renegotiate the terms of their loans from the USA, the British sent Sir Otto Niemeyer to Australia to prevent Australia doing the same for its loans from Britain.    The injustice and unabashed hypocrisy of this – where you stood on the issue of debt repayment clearly depending on where you sat – always angered me.    Had I been around in 1932, I would have supported New South Wales Premier Jack Lang’s refusal to hand over moneys from the NSW State Government owed to the Australian Commonwealth Government for its payment of interest on NSW foreign debts.

We seem to be in for more hypocrisy and hard times, as the share-owning class, having received bailouts from western taxpayers for their investments in failed and paralyzed banks, now raise a wacka wacka huna kuna against public sector debt.    The plain people of Ireland, for example, will now be paying for the malfeasance and incompetence of their richer compatriots.

Two illuminating posts from Brad DeLong and Paul Krugman on our failed western political system, which seems unable to fix our failed economy, despite us knowing what should be done:

And here is Barry Eichengreen on the Irish bailout:

Some older articles on the crisis:

 

 

 




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.




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.




Herbert Hoover, zombie

Published
by
peter
on 2010-10-23
in Economics and Politics
. Closed

I posted last week on Robert Skidelsky’s criticisms of the current British Government’s deflationary economic policy for lacking any rational theoretical underpinning.   Two Nobelistas have now joined the fray.  Here is Joe Stiglitz, writing about the apparent belief in a Confidence Fairy:

There is a shortage of aggregate demand – the demand for goods and services that generates jobs. Cutbacks in government spending will mean lower output and higher unemployment, unless something else fills the gap. Monetary policy won’t. Short-term interest rates can’t go any lower, and quantitative easing is not likely to substantially reduce the long-term interest rates government pays – and is even less likely to lead to substantial increases either in consumption or investment. If only one country does it, it might hope to gain an advantage through the weakening of its currency; but if anything the US is more likely to succeed in weakening its currency against sterling through its aggressive quantitative easing, worsening Britain’s trade position.

Of course if Britain succeeds in getting the world to believe that its economic policies are among the worst – an admittedly fierce contest at the moment – its currency may decline, but this is hardly the road to a recovery. Besides, in the malaise into which the global economy is sinking, the challenge will be to maintain exports; they can’t be relied on as a substitute for domestic demand. The few instances where small countries managed to grow in the face of austerity were those where their trading partners were experiencing a boom.

. . . .

Britain is embarking on a highly risky experiment. More likely than not, it will add one more data point to the well- established result that austerity in the midst of a downturn lowers GDP and increases unemployment, and excessive austerity can have long-lasting effects.

If Britain were wealthier, or if the prospects of success were greater, it might be a risk worth taking. But it is a gamble with almost no potential upside. Austerity is a gamble which Britain can ill afford.

And here is Paul Krugman, accusing the  British Government of being dedicated followers of fashion:

In the spring of 2010, fiscal austerity became fashionable. I use the term advisedly: the sudden consensus among Very Serious People that everyone must balance budgets now now now wasn’t based on any kind of careful analysis. It was more like a fad, something everyone professed to believe because that was what the in-crowd was saying.

. . . .

But trendy fashion, almost by definition, isn’t sensible — and the British government seems determined to ignore the lessons of history.

Both the new British budget announced on Wednesday and the rhetoric that accompanied the announcement might have come straight from the desk of Andrew Mellon, the Treasury secretary who told President Herbert Hoover to fight the Depression by liquidating the farmers, liquidating the workers, and driving down wages. Or if you prefer more British precedents, it echoes the Snowden budget of 1931, which tried to restore confidence but ended up deepening the economic crisis.

The British government’s plan is bold, say the pundits — and so it is. But it boldly goes in exactly the wrong direction. It would cut government employment by 490,000 workers — the equivalent of almost three million layoffs in the United States — at a time when the private sector is in no position to provide alternative employment. It would slash spending at a time when private demand isn’t at all ready to take up the slack.

Why is the British government doing this? The real reason has a lot to do with ideology: the Tories are using the deficit as an excuse to downsize the welfare state. But the official rationale is that there is no alternative.

Indeed, there has been a noticeable change in the rhetoric of the government of Prime Minister David Cameron over the past few weeks — a shift from hope to fear. In his speech announcing the budget plan, George Osborne, the chancellor of the Exchequer, seemed to have given up on the confidence fairy — that is, on claims that the plan would have positive effects on employment and growth.

Instead, it was all about the apocalypse looming if Britain failed to go down this route. Never mind that British debt as a percentage of national income is actually below its historical average; never mind that British interest rates stayed low even as the nation’s budget deficit soared, reflecting the belief of investors that the country can and will get its finances under control. Britain, declared Mr. Osborne, was on the “brink of bankruptcy.”

What happens now? Maybe Britain will get lucky, and something will come along to rescue the economy. But the best guess is that Britain in 2011 will look like Britain in 1931, or the United States in 1937, or Japan in 1997. That is, premature fiscal austerity will lead to a renewed economic slump. As always, those who refuse to learn from the past are doomed to repeat it.

Pity for all of us here, there’s no there there in current UK economic policy.




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