Archive for the 'Argumentation' Category

Popper vs Kuhn

For Popper scientific communities are politically virtuous because they permit unfettered criticism.   A scientific community is, by (Popper’s) definition, an open society.  Kuhn had to be shouted down because he seemed to deny this claim.”

Page 920 of B. Larvor [2000]:   Review of I. Lakatos and P. Feyerabend: “For and Against Method“. British Journal for the Philosophy of Science, 51: 919-922.




The Yirrkala Bark Petition

Australia has just commemorated the 50th anniversary of the presentation of the Yirrkala bark panels as a petition to the Australian Commonwealth Parliament in August 1963.   The panels are an example of visual art as argument, as I noted here.

Related posts on visual arguments here and here.  

 




Self-rebutting arguments

The Bank of England has been criticized because the next person selected to appear on English banknotes – Winston Churchill, on the 5-pound note – is again male, which will mean that all the figures selected to appear on English banknotes for their contributions to society will be men.   Caroline Criado-Perez has begun a legal campaign against this blatent sexism, and all power to her.

One response she has apparently encountered is that this matter is too trivial an issue for anyone to be concerned about.  But that particular argument is self-rebutting:  If the placement of images of women on national banknotes is trivial and without significant material consequences, then why not do it?!    The strength of the Bank of England’s dismissal of her campaign would seem to indicate that – to them, at least – the matter is not at all trivial.    Perhaps we should not be surprised by antediluvial attitudes to gender from an organization whose front-doormen still dress in 18th-century clothes and top-hats.

The stated criteria for appearing on banknotes apparently includes: “the person should not be controversial”.  How, then, I wonder did Winston Churchill, to this day distrusted in Australia as the chief architect of the disaster at Gallipoli, find himself selected?




Thurston on mathematical proof

Bill Thurston

2012 saw the death of Bill Thurston, leading geometer and Fields Medalist.   Learning of his death led me to re-read his famous 1994 AMS paper on the social nature of mathematical proof.   In my opinion, Thurston demolished the views of those who thought mathematics is anything other than socially-constructed.  This post is just to present a couple of long quotes from the paper. 

Continue reading ‘Thurston on mathematical proof’




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.)




Visual Reasoning

Robin Boyd called the prevailing post-war urban style of anglo-saxon architecture “Featurism”, with each building shouting to passers-by, “Me!  Me!  Look at Me!”.    Such self-promotion contrasts markedly with the dialectical approach of continental European architecture, where buildings engage in dialogue with the buildings and spaces around them.   A nice example of the latter can be found in Liverpool, UK.

The Foundation Building is a modern, glass-fronted office building between the Metropolitan (Catholic) Cathedral in Liverpool and the University of Liverpool.  Since its private-sector construction in 2006, it has been occupied by the senior administration of the University.

Upon first seeing it, I was intrigued by the 6 columns that navigate its semi-circular front.  Why are there exactly 6 columns, and why are 4 of them equidistant, while the last 2 (shown here on the right of the photo) are much closer together?   Such design decisions are rarely arbitrary; either there are engineering reasons for them or they indicate some great architectural subtlety.  In this case, it the latter reason.

For, just across the street is the red-brick Mountford Hall, dating from 1911, and now part of the University of Liverpool’s Guild of Students.

The street facade of this building has a semi-circular first-floor balcony, supported by 4 equidistant columns, and a ceremonial front door, supported by 2 columns much closer together.  The door is on the left in this photo, directly opposite the 2 closer columns on the Foundation Building.

I note here the architectural tip of the hat by the designer of the Foundation Building, and thank him or her for this pleasing subtlety.




The epistemology of intelligence

I have in the past discussed some of the epistemological challenges facing an intelligence agency – here and here.  I now see that I am not the only person to think about these matters, and that academic philosophers have started to write articles for learned journals on the topic, eg,  Herbert (2006) and Dreisbach (2011).

In essence, Herbert makes a standard argument from the philosophy of knowledge:  that knowledge (by someone of some proposition p) comprises three necessary elements:  belief by the someone in p, p being true, and a justification by the someone for his/her belief in p.  The first very obvious criticism of this approach, particularly in intelligence work, is that answering the question, Is p true? is surely the objective of any analysis, not its starting point.     A person (or an organization) may have numerous beliefs about which he (she or it) cannot say whether or not the propositions in question are true or not.  Any justification is an attempt to generate a judgement about whether or not the propositions should be believed, so saying that one can only know something when it is also true has everything pointing exactly in the wrong direction, putting the cart before the horse. This is defining knowledge to be something almost impossible to verify, and is akin to the conflict between constructivist and non-constructivist mathematicians.  How else can we know something is true except by some adequate process of justification,  so our only knowledge surely comprises justified belief, rather than justified true belief.   I think the essential problem here is that all knowledge, except perhaps some conclusions drawn using deduction, is uncertain, and this standard philosophical approach simply ignores uncertainty.

Dreisbach presents other criticisms (also long-standing) of the justified true belief model of knowledge, but both authors ignore a more fundamental  problem with this approach.   That is that much of intelligence activity aims to identify the intentions of other actors, be they states (such as the USSR or Iraq), or groups and individuals (such as potential terrorists).   Intentions, as any marketing researcher can tell you, are very slippery things:  Even a person having, or believed by others to have, an intention may not realize they have it, or may not understand themselves well enough to realize they have it, or may not be able to express to others that they have it, even when they do realize they have it.   Moreover, intentions about the non-immediate future are particularly slippery:  you can ask potential purchasers of some new gizmo all you want before the gizmo is for sale, and still learn nothing accurate about how those very same purchasers will actually react when they are able to finally purchase it.  In short, there is no fact of the matter with intentions, and thus it makes no sense to represent them as propositions.  Accordingly, we cannot evaluate whether or not p is true, so the justified true belief model collapses.  It would be better to ask (as good marketing researchers do):    Does the person in question have a strong tendency to act in future in a certain way, and if so, what factors will likely encourage or inhibit or preclude them to act that way?

However, a larger problem looms with both these papers, since both are written as if the respective author believes the primary purpose of intelligence analysis is to garner knowledge in a vacuum.      Knowledge is an intermediate objective of intelligence activity, but it is surely subordinate to the wider diplomatic, military or political objectives of the government or society the intelligence activity is part of.  CIA was not collecting information about the USSR, for example, because of a disinterested, ivory-tower-ish concern with the progress of socialism in one country, but because the USA and the USSR were engaged in a global conflict.    Accordingly, there are no neutral actions – every action, every policy, every statement, even every belief of each side may have consequences for the larger strategic interaction that the two sides are engaged in.   A rational and effective intelligence agency should not just be asking:

Is p true?

but also:

  • What are the consequences of us believing p to be true?
  • What are the consequences of us believing p not to be true?
  • What are the consequences of the other side believing that we believe p to be true?
  • What are the consequences of the other side believing that we do not believe p to be true?
  • What are the consequences of the other side believing that we are conflicted internally about the truth of p?
  • What are the consequences of the other side initially believing that we believe p to be true and then coming to believe that we do not believe p?
  • What are the consequences of the other side initially believing that we do not believe p to be true and then coming to believe that we do in fact believe p?
  • What are the consequences of the other side being conflicted about whether or not they should believe p?
  • What are the consequences of the other side being conflicted about whether or not we believe p?

and so on.   I give an example of the possible strategic interplay between a protagonist’s beliefs and his or her antagonist’s intentions here.

A decision to believe or not believe p may then become a strategic one, taken after analysis of these various consequences and their implications.   An effective intelligence agency, of course, will need to keep separate accounts for what it really believes and what it wants others to believe it believes.  This can result in all sorts of organizational schizophrenia, hidden agendas, and paranoia (Holzman 2008), with consequent challenges for those writing histories of espionage.  Call these mind-games if you wish, but such analyses helped the British manipulate and eventually control Nazi German remote intelligence efforts in British and other allied territory during World War II (through the famous XX system).

Likewise, many later intelligence efforts from all major participants in the Cold War were attempts –some successful, some not – to manipulate the beliefs of opponents.   The Nosenko case (Bagley 2007) is perhaps the most famous of these, but there were many.   In the context of the XX action, it is worth mentioning that the USA landed scores of teams of spies and saboteurs into the Democratic Republic of Vietnam (North Vietnam) during the Second Indochinese War, only to have every single team either be captured and executed, or captured and turned; only the use of secret duress codes by some landed agents communicating back enabled the USA to infer that these agents were being played by their DRV captors.

Intelligence activities are about the larger strategic interaction between the relevant stakeholders as much (or more) than they are about the truth of propositions.  Neither Herbert nor Dreisbach seems to grasp this, which makes their analysis disappointingly impoverished.

References:

Tennent H. Bagley [2007]:  Spy Wars.  New Haven, CT, USA:  Yale University Press.

Christopher Dreisbach [2011]:  The challenges facing an IC epistemologist-in-residence.  International Journal of Intelligence and CounterIntelligence, 24: 757-792.

Matthew Herbert [2006]:  The intelligence analyst as epistemologist.  International Journal of Intelligence and CounterIntelligence, 19:  666-684.

Michael Holzman [2008]:  James Jesus Angleton, the CIA and the Craft of Counterintelligence.  Boston, MA, USA: University of Massachusetts Press.




Oral culture

For about 300 years, and especially from the introduction of universal public education in the late 19th century, western culture has  been dominated by text and writing.  Elizabethan culture, by contrast, was primarily oral:  Shakespeare, for example, wrote his plays to be performed not to be read, and did not even bother to arrange definitive versions for printing.   One instance of the culture-wide turn from speech to text was a switch from spoken to written mathematics tests in the west which occurred at Cambridge in the late 18th century, as I discuss here.  There is nothing intrinsically better about written examinations over spoken ones, especially when standardized and not tailored for each particular student.  This is true even for mathematics, as is shown by the fact that oral exams are still the norm in university mathematics courses in the Russian-speaking world; Russia continues to produce outstanding mathematicians.

Adventurer and writer Rory Stewart, now an MP,  has an interesting post about the oral culture of the British Houses of Parliament, perhaps the last strong-hold of argument-through-speech in public culture.  The only other places in modern life, a place which is not quite as public, where speech reigns supreme, are court rooms.




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.