by Prof. Peter McBurney - p.j.mcburney [at] csc.liv.ac.uk

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Most real-world business decisions are considerably more complex than the example decisions discussed 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 decisions?

• There are fundamental domain uncertainties.

  None of us know 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 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, 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 by a democracy may only be successful if they 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 committments with suppliers and even installing equipment ahead of knowing the outcome of a bid, and even ahead of deciding whether or not to bid.

• The consequences of decisions may be slow to realize.

  The oil industry usually works on 50+ year cycles for major investment projects. Satellite mobile communications networks have typically taken ten years from serious inception to launch of service.

• 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 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.