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The science of delegation

Most people, if they think about the topic at all, probably imagine computer science involves the programming of computers.  But what are computers?  In most cases, these are just machines of one form or another.  And what is programming?  Well, it is the issuing of instructions (“commands” in the programming jargon) for the machine to do something or other, or to achieve some state or other.   Thus, I view Computer Science as nothing more or less than the science of delegation.

When delegating a task to another person, we are likely to be more effective (as the delegator or commander) the more we know about the skills and capabilities and curent commitments and attitudes of that person (the delegatee or commandee).   So too with delegating to machines.   Accordingly, a large part of theoretical computer science is concerned with exploring the properties of machines, or rather, the deductive properties of mathematical models of machines.  Other parts of the discipline concern the properties of languages for commanding machines, including their meaning (their semantics) – this is programming language theory.  Because the vast majority of lines of program code nowadays are written by teams of programmers, not individuals, then much of computer science – part of the branch known as software engineering – is concerned with how to best organize and manage and evaluate the work of teams of people.   Because most machines are controlled by humans and act in concert for or with or to humans, then another, related branch of this science of delegation deals with the study of human-machine interactions.   In both these branches, computer science reveals itself to have a side which connects directly with the human and social sciences, something not true of the other sciences often grouped with Computer Science: pure mathematics, physics, or chemistry. 

And from its modern beginnings 70 years ago, computer science has been concerned with trying to automate whatever can be automated – in other words, with delegating the task of delegating.  This is the branch known as Artificial Intelligence.   We have intelligent machines which can command other machines, and manage and control them in the same way that humans could.   But not all bilateral relationships between machines are those of commander-and-subordinate.  More often, in distributed networks machines are peers of one another, intelligent and autonomous (to varying degrees).  Thus, commanding is useless – persuasion is what is needed for one intelligent machine to ensure that another machine does what the first desires.  And so, as one would expect in a science of delegation, computational argumentation arises as an important area of study.

 




Strategic Progamming

Over the last 40-odd years, a branch of Artificial Intelligence called AI Planning has developed.  One way to view Planning is as automated computer programming: 

  • Write a program that takes as input an initial state, a final state (“a goal”), and a collection of possible atomic actions, and  produces as output another computer programme comprising a combination of the actions (“a plan”) guaranteed to take us from the initial state to the final state. 

A prototypical example is robot motion:  Given an initial position (e.g., here), a means of locomotion (e.g., the robot can walk), and a desired end-position (e.g., over there), AI Planning seeks to empower the robot to develop a plan to walk from here to over there.   If some or all the actions are non-deterministic, or if there are other possibly intervening effects in the world, then the “guaranteed” modality may be replaced by a “likely” modality. 

Another way to view Planning is in contrast to Scheduling:

  • Scheduling is the orderly arrangement of a collection of tasks guranteed to achieve some goal from some initial state, when we know in advance the initial state, the goal state, and the tasks.
  • Planning is the identification and orderly arrangement of tasks guranteed to achieve some goal from some initial state, when we know in advance the initial state, the goal state, but we don’t yet know the tasks;  we only know in advance the atomic actions from which tasks may be constructed.

Relating these ideas to my business experience, I realized that a large swathe of complex planning activities in large companies involves something at a higher level of abstraction.  Henry Mintzberg called these activities “Strategic Programming”

  • Strategic Programming is the identification and priorization of a finite collection of programs or plans, given an initial state, a set of desirable end-states or objectives (possibly conflicting).  A program comprises an ordered collection of tasks, and these tasks and their ordering we may or may not know in advance.

Examples abound in complex business domains.   You wake up one morning to find yourself the owner of a national mobile telecommunications licence, and with funds to launch a network.  You have to buy the necessary equipment and deploy and connect it, in order to provide your new mobile network.   Your first decision is where to provide coverage:  you could aim to provide nationwide coverage, and not open your service to the public until the network has been installed and connected nationwide.  This is the strategy Orange adopted when launching PCS services in mainland Britain in 1994.   One downside of waiting till you’ve covered the nation before selling any service to customers is that revenues are delayed. 

Another downside is that a competitor may launch service before you, and that happened to Orange:  Mercury One2One (as it then was) offered service to the public in 1993, when they had only covered the area around London.   The upside of that strategy for One2One was early revenues.  The downside was that customers could not use their phones outside the island of coverage, essentially inside the M25 ring-road.   For some customer segments, wide-area or nationwide coverage may not be very important, so an early launch may be appropriate if those customer segments are being targeted.  But an early launch won’t help customers who need wider-area coverage, and – unless marketing communications are handled carefully – the early launch may position the network operator in the minds of such customers as permanently providing inadequate service.   The expectations of both current target customers and customers who are currently targets need to be explicitly managed to avoid such mis-perceptions.

In this example, the different coverage rollout strategies ended up at the same place eventually, with both networks providing nationwide coverage.  But the two operators took different paths to that same end-state.   How to identify, compare, prioritize, and select-between these different paths is the very stuff of marketing and business strategy, ie, of strategic programming.  It is why business decision-making is often very complex and often intellectually very demanding.   Let no one say (as academics are wont to do) that decision-making in business is a doddle.   Everything is always more complicated than it looks from outside, and identifying and choosing-between alternative programs is among the most complex of decision-making activities.




Combining actions

How might two actions be combined?  Well, depending on the actions, we may be able to do one action and then the other, or we may be able do the other and then the one, or maybe not.  We call such a combination a sequence or concatenation of the two actions.  In some cases, we may be able to do the two actions in parallel, both at the same time.  We may have to start them simultaneously, or we may be able to start one before the other.  Or, we may have to ensure they finish together, or that they jointly meet some other intermediate synchronization targets.

In some cases, we may be able to interleave them, doing part of one action, then part of the second, then part of the first again, what management consultants in telecommunications call multiplexing.   For many human physical activities – such as learning to play the piano or learning to play golf – interleaving is how parallel activities are first learnt and complex motor skills acquired:  first play a few bars of music on the piano with only the left hand, then the same bars with only the right, and keep practicing the hands on their own, and only after the two hands are each practiced individually do we try playing the piano with the two hands together.

Computer science, which I view as the science of delegation, knows a great deal about how actions may be combined, how they may be distributed across multiple actors, and what the meanings and consequences of these different combinations are likely to be.     It is useful to have a list of the possibilities.  Let us suppose we have two actions, represented by A and B respectively.   Then we may be able to do the following compound actions:

  • Sequence:  The execution of A followed by the execution of B, denoted A ;  B
  • Iterate: A executed n times, denoted A ^ n  (This is sequential execution of a single action.)
  • Parallelize: Both A and B are executed together, denoted A & B
  • Interleave:  Action A is partly executed, followed by part-execution of B, followed by continued part-execution of A, etc, denoted A || B
  • Choose:  Either A is executed or B is executed but not both, denoted A v B
  • Combinations of the above:  For example, with interleaving, only one action is ever being executed at one time.  But it may be that the part-executions of A and B can overlap, so we have a combination of Parallel and Interleaved compositions of A and B.

Depending on the nature of the domain and the nature of the actions, not all of these compound actions may necessarily  be possible.  For instance, if action B has some pre-conditions before it can be executed, then the prior execution of A has to successfully achieve these pre-conditions in order for the sequence A ; B to be feasible.

This stuff may seem very nitty-gritty, but anyone who’s ever asked a teenager to do some task they don’t wish to do, will know all the variations in which a required task can be done after, or alongside, or intermittently with, or be replaced instead by, some other task the teen would prefer to do.    Machines, it turns out, are much like recalcitrant and literal-minded teenagers when it comes to commanding them to do stuff.




The semantics of communication

A recent incident reminded me of Nicolas Negroponte’s argument that a single wink (one bit of information) may require a thousand words to explain to someone else.

The scene: A small group meeting of 5 people, none of whom know each other or have worked together before. The meeting chair, let’s call her Alice, wants another person, Bob, to endorse a particular outline plan of action. This plan does not entail him doing anything, but he is nonetheless resistant, and puts forward both reasonable and unreasonable justifications for not endorsing the plan. Alice tries another couple of arguments, but each of these meets similar resistance from Bob. At this point, Alice does not know what the rest of us think about her plan or Bob’s opinion of it.

Having heard the two sides, I decide that Alice is correct and that Bob should endorse the plan. But Alice, I believe, has not used the best arguments in favour of his doing so, and thus I add my voice to her side, giving a new argument to justify Bob changing his opinion. My argument fails with Bob, but leads Alice to think of a further argument, and both our arguments together have a consequence that completely rebuts Bob’s reasonable main defence for non-endorsement. When she presents this line (my argument + her argument + their joint consequence) to him, Bob wilts and agrees to endorse Alice’s plan.

However, just before Alice presents this line to Bob, she shoots me a quick look of conspiratorial deviousness, as if to say, “We got him, you and I, and in getting him, we have demonstrated our intellectual superiority and mental agility over him. Although we just met, we two have conspired effectively and enjoyably together.” It was a look of the most profound respect – a connection between equals, in the presence of someone whose persistent and unreasonable resistance to a reasonable proposal had revealed himself to be less committed to the agreed purpose of the meeting.  And receiving it was the most profound of pleasures.




Command Dialogs

Three years ago, in a post about Generation Kill and Nate Fick, I remarked that military commands often need dialog between commander and commandee(s) before they may be rationally accepted, and/or executed.   Sadly, a very good demonstration of the failure to adequately discuss commands (or purported commands) in a complex (police) action is shown by a report on the UC-Davis Pepper Spray incident.  

Management textbooks of a certain vintage used to define management as the doing of things through others.   The Pepper Spray example clearly shows the difficulties and challenges involved in actually achieving such vicarious doing in dynamic and ambiguous situations.  And the poverty of Philosophy is not better shown than by the fact that the speech act of commanding has barely been studied at all by philosophers, obsessed these last 2,350 years with understanding assertions of facts.  (Chellas, Hamblin, Girle and Parsons are exceptions.)




What use are models?

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.




The otherness of the other

In previous posts (eg, here and here), I have talked about the difficulty of assessing the intentions of others, whether for marketing or for computer network design or for national security. The standard English phrase speaks of “putting ourselves in the other person’s shoes”.  But this is usually not sufficient:  we have to put them into their shoes, with their beliefs, their history, their desires, and their constraints, not ourselves, in order to understand their goals and intentions, and to anticipate their likely strategies and actions.    In a fine political thriller by Henry Porter, I come across this statement (page 220):

‘Motive is always difficult to read,’ he replied.  ‘We make a rational assumption about someone’s behaviour based on what we would, or would not, do in the same circumstances, ignoring the otherness of the other. We consider only influences that make us what we are and impose those beliefs on them.  It is the classic mistake of intelligence analysis.’  “

Reference:

Henry Porter [2009]: The Dying Light. London, UK:  Orion Books.

Obscure fact:  Porter (born 1953) is the grand-nephew of novelist Howard Sturgis (1855-1920), step-cousin to George Santayana (1863-1952).




On Getting Things Done

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 IntelligenceOne 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.   The golden age of television drama we are currently fortunate to be witness to also provides good training for viewers in human motivations, 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.




Coupling preferences and decision-processes

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.