• 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.

Technorati Tags: game theory, decision theory, network goods, reflexivity

I have a profound philosophical disagreement with this objective, and indeed with most of the EC’s many efforts in standardization.   Tim Berners-Lee invented Hyper-Text Transfer Protocol (HTTP), for example, in order to enable physicists to publish their research documents to one another in a manner which enabled author-control of document appearance.    Like most new technologies. HTTP was not invented for the many other uses to which it has since been put; indeed, many of these other applications have required hacks or fudges to HTTP in order to work.  For example, because HTTP does not keep track of the state of a request, fudges such as cookies are needed.  If we had all been in consensual agreement with The Greatest Living Briton about the purposes of HTTP, we would have no e-commerce, no blogging, no social networking, no easy remote access to databases, no large-scale distributed collaborations, no easy action-at-a-distance, in short no transformation of our society and life these last two decades, just the broadcast publishing of text documents. 

Let us put aside this childish, warm-and-fuzzy, touchy-feely seeking after consensus.  Our society benefits most from a diversity of opinions and strong disagreements, a hundred flowers blooming, a cacophony of voices in the words of Oliver Wendell Holmes.  This is particularly true of opinions regarding the uses and applications of innovations.   Yet the EC persists, in some recalcitrant chasing after illusive certainty, in trying to force us all into straitjackets of standards and equal practice.    These efforts are misguided and wrong-headed, and deserve to fail.

Technorati Tags: Future Internet, HTTP, Oliver Wendell Holmes

What might have seemed, for example,  mere pointless musings on the correct way to undertake reasoning – by Aristotle, by Islamic and Roman Catholic medieval theologians, by numerous English, Irish and American abstract mathematicians in the 19th century, by an entire generation of Polish logicians before World War II, and by those real-world men-of-action Gottlob Frege, Bertrand Russell, Ludwig Wittgenstein and Alonzo Church – turned out to be EXTREMELY USEFUL for the design and engineering of electronic computers.   Despite Russell’s Zen-influenced personal motto – “Just do!  Don’t think!” (later adopted by IBM) – his work turned out to be useful after all.   I can see the British research funding agencies right now, using their sophisticated and proven prognostication procedures to calculate the society-wide economic and social benefits we should expect to see from our current research efforts over the next 2300 years  – ie, the length of time that Aristotle’s research on logic took to be implemented in technology.   Thank goodness our politicians have shown no myopic utilitarianism this last couple of centuries, eh what?!

All while this man apparently received no direct state or commercial research funding for his efforts as a computer pioneer, playing with “pointless” abstractions like the lambda calculus.

And Normblog also comments.

Technorati Tags: utilitarianism, lambda calculus

Let me start by repeating firmly what I said before (here abbreviated), as preface:

• Owners of companies (including the present British Government) have the legal and moral right to establish any legal payment policies they so wish. 
• People who fail should not be rewarded for that failure.

Then let me repeat this para:

Finally, in all the nonsense spoken about a “bonus culture” in banks, I have nowhere seen any discussion as to WHY bonus payments are common in the financial world.   The first reason is that financial markets are capricious:  despite all the boastful talk about skills and experience, and despite the multi-terabytes of computer memory, the massively-high bandwidths of connection, and the arrays of rocket scientists deployed, the taking of positions in markets is still a matter of taking views on the future, and betting these views against other views.   The future is uncertain, so bets can lose, as well as win,  and these two outcomes may happen regardless of the skills or expertise or resources applied to the taking of a view.  People may be just lucky or unlucky – even clever, experienced, well-resourced, cautious, nice people.    The second reason is that when bets win, they may win big.    If  a company makes hundreds of millions of dollars profit from a one or a handful of trades, it can seem most unfair to those deciding what trades to do that all of this capricious, windfall gain should accrue just to the shareholders.  Those doing the trading therefore ask, quite reasonably in my opinion, for a share of this windfall gain.   Most companies have then a choice:  give a few percent of these capricious windfalls to the staff doing the work as bonus payments, or risk losing the staff to the competitor down the street who will.   No rational, long-term manager would choose the latter option, and no rational, long-term shareholder would force him or her to.

There is another aspect to the business of the financial sector that I overlooked in this description.  For most of us working in companies, our actions have impacts on the revenues, costs and profits of the company only indirectly.  Typically, the larger the organization the more indirect the effects.  A product manager for one of the thousands of consumer products at, say, Proctor and Gamble, for instance may have great ideas for innovative product development, ideas which, when implemented, lead to massive increases in corporate revenues.  But she would not be able to develop these ideas, create the necessary support infrastructures (which may include product redesigns, or new manufacturing facilities, or new marketing campaigns) and then execute them, without help and support from a very large number of people; perhaps as many as tens of thousands of people would be involved for the most complex of products, from research scientists through to traveling salespeople in equatorial Africa.   Success in such environments requires real – and all too rare – skills at group-working, information sharing, socialization, co-ordination, complex project planning, and managing of others (in all directions, below, above and laterally), both inside one’s own company and in partner companies.  These abilities are required in addition to whatever linguistic and/or mathematical intelligence required (which can be considerable for any high-tech product and even for ordinary consumer durables) and so-called emotional intelligence.   This is a getting-things-done intelligence, a skill-set that otherwise very intelligent and successful people may lack, for example, academics, writers, medical professionals.

Strangely enough, few bankers, in my experience, need these getting-things-done abilities either, and few have them.  The large sums in finance, which generate the large bonuses, usually accrue (as I said above) to successful bets on the future.  To make such bets you need: (a) to take a view on the future (a view which may arise from sophisticated, complex and expensive computer modeling) and (b) money to bet with.  You don’t normally need a large team behind you, although computer modeling may require rare and specialised – and hence, expensive – skills. You don’t need much equipment, just a laptop and access to processing power (now rentable by the minute); you certainly don’t need a factory, or a global distribution network, or a boffin-filled R&D lab, as the product managers for P&G do.  Apart from the money used to bet with, you don’t need much money either.   You may need, as sociologist Donald MacKenzie has shown in his detailed fieldwork studies of financial institutions, a good network of contacts, both in your own organization and outside it, of people whom you trust you and who trust you; you need this network in order to be able to accurately predict financial market movements, and to predict how market participants would be likely to react to new information, in other words, to help you to take a view on the future.

So, suppose you work for a large financial institution in the City of London, betting on (say) US Dollar-to-Japanese Yen exchange rates.  You come into work, you read the newsfeeds, you look at the numbers, you talk with your network, you (or your team) do your computer simulations, you take a view of the future, and you place your bet.  (You may do this with any frequency, from every few weeks to every few minutes, depending on the market you are in and the strategies you running.)   The money you use to bet with belongs to your employer.  Perhaps you bet the value of the USD will rise in comparison to the Yen, as a result of the recent Japanese election.   Or perhaps you bet that most other exchange traders will bet that it will rise for this reason, and so you would profit from betting against them.  For whatever reason – good judgment, intuition, experience, clever algorithms, computer processing power, speed of reaction, personal network connectedness, luck, clever accounting, inside information, mojo, guidance from the beyond - your bets are successful, and thus you (and perhaps your small team) generate large revenues for your employer.   Unlike our friend at P&G, no one can say these revenues depended on the supportive and co-ordinated activities of hundreds or thousands of people working over months or years.  No one can say that your employer would have got the revenues anyway, even without you.  And given the few required inputs listed in the previous paragraph, no one can stop you walking out of your office one afternoon and offering your successful judgment (your view-taking ability) to some other employer with money to bet.  As the saying goes, the bank’s key assets walk out the front door each evening.

In other words, large bonuses exist in the financial world because large revenue streams exist which are directly traceable to the actions of a small number of people, and because not paying good bonuses to these people risks the employer losing the revenue streams.   None of this is evidence of malice, or evil, or some malcontent recalcitrance on the part of bankers, failing to reform despite the bailout.  It is the very nature of business under capitalism.  Employees are acting rationally and ethically in asking for such bonuses, and employers in giving them.  Large bonuses would exist in other industries if the conditions were right; indeed, they do, for senior managers in IPOs and acquisitions, or for Hollywood stars, able to guarantee large audiences for the opening weekends of films.  In both these cases, as with finance, large money streams can be traced directly to the actions of a small number of people.   If you were to make it illegal for banks to pay bonuses, then for consistency’s sake, you’d have to also tell JK Rowling to give up her millions.  Arguing that bonuses should not exist will be about as successful as arguing that water should not run downhill, and is, in my opinion, about as rational.

Reference:

Iain Hardie and Donald MacKenzie [2007]: Assembling an economic actor: the agencement of a Hedge Fund. The Sociological Review, 55 (1): 57-80.

But now the second generation of social networking forecasting tools has arrived.  I see that a French start-up, Doppio Software, has recently launched publicly.   They appear to have a product which has several advantages over ours:

• Doppio Software is focused on forecasting demand along a supply chain.  This means the forecasting objective is very tactical, not the long-term strategic forecasting that CO2 emission permit prices became.   In the present economic climate, short-term tactical success is certainly more compelling to business customers than even looking five years hence.
• The relevant social network for a supply chain is a much stronger community of interest than the amorphous groups we had in mind for Prophets-360.  Firstly, this community already exists (for each chain), and does not need to be created.  Secondly, the members of the community by definition have differential access to information, on the basis of their different positions up and down the chain.  Thirdly, although the interests of the partners in a supply chain are not identical, these interests are mutually-reinforcing:  everyone in the chain benefits if the chain itself is more successful at forecasting throughput.
• In addition, Team Doppio (the Doppiogangers?) appear to have included a very compelling value-add:  their own automated modeling of causal relationships between the target demand variables of each client and general macro-economic variables, using  semantic-web data and qualitative modeling technologies from AI.  Only the largest manufacturing companies can afford their own econometricians, and such people will normally only be able to hand-craft models for the most important variables.  There are few companies IMO who would not benefit from Doppio’s offer here.

Of course, I’ve not seen the Doppio interface and a lot will hinge on its ease-of-use (as with all software aimed at business users).  But this offer appears to be very sophisticated, well-crafted and compelling, combining social network forecasting, intelligent causal modeling and semantic web technologies.

Well done, Team Doppio!  I wish you every success with this product!

PS:  I have just learnt that “doppio” means “double”, which makes it a very apposite name for this application – forecasts considered by many people, across their human network.  Neat!  (2009-09-16)

Article in The Observer (UK) about Doppio 2009-09-06 here.

Technorati Tags: Web 2.0, social-network forecasting, greenhouse gases, carbon emission, Doppio Software, causal modeling, semantic web technologies

Nassim Taleb has an article in the FinTimes presenting ten principles he believes would reduce the occurrence of rare, catastrophic events (events he has taken to calling black swans).  Many of his principles are not actionable, and several are ill-advised.  Take, for instance, # 3:

3. People who were driving a school bus blindfolded (and crashed it) should never be given a new bus.

If this principle was applied, the bus would have no drivers at all.   All of us are driving blindfolded, with our only guide to the road ahead being what we can apprehend from the rear-view mirror.  Past performance, as they say, is no guide to the future direction of the road.

Or take #6:

6. Do not give children sticks of dynamite, even if they come with a warning.  Complex derivatives need to be banned because nobody understands them and few are rational enough to know it. Citizens must be protected from themselves, from bankers selling them “hedging” products, and from gullible regulators who listen to economic theorists.

Well, what precisely is “complex”?  Surely, Dr Taleb is not suggesting the banning of plain futures and options, as these serve a valuable function in our economy (enabling the parceling and trading of risk).  But even these are too complex for some people (such as those farmers, dentists, and local government officials currently with burnt fingers), and surely such people need protection from themselves much more so than the quant-jocks and their masters on Wall Street.  So, where would one draw the line between allowed derivative and disallowed? 

Once again, it appears there has been a mis-understanding of the cause of the recent problems.  It is not complex derivatives per se that are the problem, but the fact that many of these financial instruments have, unusually, been highly-correlated.  Thus, the failure of one instrument (and subsequently, one bank) brings down all the others with it — there is a systemic risk as well as a participant risk involved in their use.   Dr Taleb, who has long been a critic of the unthinking use of Gaussian models in finance, I am sure realises this.

Technorati Tags: Nassim Taleb, complex derivatives, Gaussian models

Reference:

Charles McCarry [1979]: The Better Angels. London, UK:  Arrow Books.

Technorati Tags: Judge Harold H Greene, Charles McCarry

 For example, although it is logically invalid to accept an argument only on the grounds that the person making it is an authority on the subject, our legal system does this all the time.  Indeed,  the philosopher Charles Willard has argued that modern society could not function without most of us accepting arguments-from-authority most of the time, and it is usually rational to do so.  Accordingly, philosophers of argumentation have investigated the conditions under which a rational person would accept or reject such arguments.   Douglas Walton (1996, pp. 64-67) presents an argumentation scheme for such acceptance/rejection decisions, the Argument Scheme for Arguments from Expert Opinion, as follows:

• Assume E is an expert in domain D.
• E asserts that statement A is known to be true.
• A is within D.

Therefore, a decision-maker may plausibly take A to be true, unless one or more of the following Critical Questons (CQ) is answered in the negative:

• CQ1:  Is E a genuine expert in D?
• CQ2:  Did E really assert A?
• CQ3:  Is A relevant to domain D?
• CQ4:  Is A consistent with what other experts in D say?
• CQ5:  Is A consistent with known evidence in D?

One could add further questions to this list, for example:

• CQ6:  Is E’s opinion offered without regard to any reward or benefit upon statement A being taken to be true by the decision-maker?

Walton himself presents some further critical questions first proposed by Augustus DeMorgan in 1847 to deal with cases under CQ2 where the expert’s opinion is presented second-hand, or in edited form, or along with the opinions of others.

 Clearly, some of these questions are also pertinent to assessing forecasts and prophecies.  But the special nature of forecasts and prophecies may enable us to make some of these questions more precise.  Here is my  Argument Scheme for Arguments from Prophecy:

• Assume E is a forecaster for domain D.
• E asserts that statement A will be true of domain D at time T in the future.
• A is within D.

Therefore, a decision-maker may plausibly take A to be true at time T, unless one or more of the following Critical Questons (CQ) is answered in the negative:

• CQ1:  Is E a genuine expert in forecasting domain D?
• CQ2:  Did E really assert that A will be true at T?
• CQ3:  Is A relevant to, and within the scope of, domain D?
• CQ4:  Is A consistent with what is said by other forecasters with expertise in D?
• CQ5:  Is A consistent with known evidence of current conditions and trends in D?
• CQ6:  Is E’s opinion offered without regard to any reward or benefit upon statement A being adopted by the decision-maker as a forecast?
• CQ7:  Do the benefits of adopting A being true at time T in D outweigh the costs of doing so, to the decision-maker?

In attempting to answer these questions, we may explore more detailed questions:

• CQ1-1:  What is E’s experience as forecaster in domain D? 
• CQ1-2: What is E’s track record as a forecaster in domain D?
• CQ2-1: Did E articulate conditions or assumptions under which A will become true at T, or under which it will not become true?  If so, what are these?
• CQ2-2:  How sensitive is the forecast of A being true at T to the conditions and assumptions made by E?
• CQ2-3:  When forecasting that A would become true at T, did E assert a more general statement than A?
• CQ2-4:  When forecasting that A would become true at T, did E assert a more general time than T?
• CQ2-5:  Is E able to provide a rational justification (for example, a computer simulation model) for the forecast that A would be true at T?
• CQ2-6:  Did E present the forecast of A being true at time T qualified by modalities, such as possibly, probably, almost surely, certainly, etc.
• CQ4-1:  If this forecast is not consistent with those of other forecasters in domain D, to what extent are they inconsistent?   Can these inconsistencies be rationally justified or explained?
• CQ5-1: What are the implications of A being true at time T in domain D?  Are these plausible?  Do they contradict any known facts or trends?
• CQ6-1:  Will E benefit if the decision-maker adopts A being true at time T as his/her forecast for domain D? 
• CQ6-2:  Will E benefit if the decision-maker does not adopt A being true at time T as his/her forecast for domain D? 
• CQ6-3:  Will E benefit if many decision-makers adopt A being true at time T as their forecast for domain D?
• CQ6-4:  Will E benefit if few decision-makers adopt A being true at time T as their forecast for domain D?
• CQ6-5:  Has E acted in such a way as to indicate that E had adopted A being true at time T as their forecast for domain D (eg, by making an investment betting that A will be true at T)?
• CQ7-1:  What are the costs and benefits to the decision-maker for adopting statement A being true at time T in domain D as his or her forecast of domain D? 
• CQ7-2:  How might these costs and benefits be compared?  Can a net benefit/cost for the decision-maker be determined?

Automating these questions and the process of answering them is on my list of next steps, because automation is needed to design machines able to reason rationally about the future.   And rational reasoning about the future is needed if  we want machines to make decisions about actions.

References:

Augustus DeMorgan [1847]: Formal Logic.  London, UK:  Taylor and Walton.

Douglas N. Walton [1996]:  Argument Schemes for Presumptive Reasoning. Mahwah, NJ, USA: Lawrence Erlbaum.

Charles A. Willard [1990]: Authority.  Informal Logic, 12: 11-22.

Technorati Tags: global financial crisis, forecasts, prophecies, argumentation, Charles Willard, arguments-from-authority, Douglas Walton, argumentation scheme, Augustus DeMorgan

Technorati Tags: Karl Marx, capitalism, socialization of the means of exchange