In July 2005, inspired by a talk on formation flying by unmanned aircraft by Sandor Veres at the Liverpool Agents in Space Symposium, I wrote down some rules of thumb I have been using informally for determining whether an agent-based modeling (ABM) approach is appropriate for a particular application domain.  Appropriateness is assessed by answering the following questions:

1. Are there multiple entities in the domain, or can the domain be represented as if there are?
2. Do the entities have access to potentially different information sources or do they have potentially different beliefs? For example, differences may be due to geographic, temporal, legal, resource or conceptual constraints on the information available to the entities.
3. Do the entities have potentially different goals or objectives? This will typically be the case if the entities are owned or instructed by different people or organizations.
4. Do the entities have potentially different preferences (or utilities) over their goals or objectives ?
5. Are the relationships between the entities likely to change over time?
6. Does a system representing the domain have multiple threads of control?

If the answers are YES to Question 1 and also YES to any other question, then an agent-based approach is appropriate. If the answer to Question 1 is NO, or if the answers are YES to Question 1 but NO to all other questions, then a traditional object-based approach is more appropriate.

Traditional object-oriented systems involve static relationships between non-autonomous entities sharing the same beliefs, preferences and goals, and in a system with a single thread of control.

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.

Charles Leonard Hamblin (1922-1985) was an Australian philosopher and one of Australia’s first computer scientists. His main early contributions to computing, which date from the mid 1950s, were the development and application of reverse polish notation and the zero-address store. He was also the developer of one of the first computer languages, GEORGE. Since his death, his ideas have become influential in the design of computer interaction protocols, and are expected to shape the next generation of e-commerce and machine-communication systems.

Hamblin was born in 1922 and attended North Sydney Boys’ High School and Geelong Grammar. He then took degrees in Arts (Philosophy and Mathematics) and in Science (Physics), followed by an MA in Philosophy (First Class Honours) at Melbourne University, with his studies interrupted by work as a radar officer in the RAAF during World War II. Following the war, he gained a PhD at the London School of Economics, University of London on the topic, “Language and the Theory of Information”, apparently under Karl Popper (Hamblin 1957a). Hamblin’s thesis presented a critique of Shannon’s theory of information from a semantic perspective, and developed a possible-worlds semantics for question-response exchanges. Between 1955 and his death in 1985 he was a Lecturer and Professor in the School of Philosophy at New South Wales University of Technology (NSWUT), which later became the University of New South Wales (UNSW).

In 1956, the University purchased a DEUCE computer manufactured by the English Electric Company (EEC), an early British computer manufacturer, and Hamblin, with his radar background, became involved in working with this machine. This was the second academic computer in Australia, after that of the University of Sydney. Hamblin soon became aware of the problems of (a) computing mathematical formulae containing brackets, and (b) the memory overhead in having dealing with memory stores each of which had its own name. One solution to the first problem was Jan Lukasiewicz’s Polish notation, which enables a writer of mathematical notation to instruct a reader the order in which to execute the operations (e.g. addition, multiplication, etc) without using brackets. Polish notation achieves this by having an operator (+, *, etc) precede the operands to which it applies, e.g., +ab, instead of the usual, a+b. Hamblin, with his training in formal logic, knew of Lukasiewicz’s work.

However, this does not solve the second problem. Hamblin realized that placing the operation symbol to the right of the operands (i.e., reversing the polish notation, as in ab+) would enable the machine to make use of a store which did not require an address – the current operation would always be conducted on the most-recent operands inserted and still remaining in the store. This store came to be called a stack, or last-in, first-out (LIFO) store. He implemented these ideas in a programming language for the DEUCE machine, a language he called GEORGE, for General Order Generator. This work was undertaken at a time when there were only a handful of programming languages, and indeed still some resistance to the idea of non-assemblor languages (due to their greater memory requirements). Hamblin’s work on the DEUCE machine at UNSW overlapped with that of Gordon Bell and Bob Brigham, who wrote a symbolic assembler and run-time system called SODA (or Symbolic Optimum DEUCE Assembly Program) (Brigham and Bell 1959). GEORGE used the SODA runtime library.

Hamblin presented his work at the first Australian conference on computing, which was held at the Weapon Research Establishment in Salisbury, South Australia, in June 1957 (Hamblin 1957b). Employees of the English Electric Company were present at this conference, and took his ideas back to England. As a consequence, Hamblin’s architecture was implemented in the company’s next machine, which came to be called the KDF9. The architecture of this machine even used Hamblin’s terminology. This machine was announced in 1960 and delivered (i.e. made available commercially) in 1963. Hamblin published his ideas in 1957 (Hamblin 1957b, 1957c) and 1962 (Hamblin 1962). An earlier paper (Burks et al. 1954) presented the same ideas in a more general notational framework, and that paper was briefly reviewed in the Journal of Symbolic Logic in 1955 (Nelson 1955). Hamblin may have seen the Burks paper, or (with greater probability) the JSL review, although neither of these articles is cited in his 1962 Computer Journal paper which presents RPN (Hamblin 1962). (When accessed on 2010-07-20, the catalogue of the Library of the University of New South Wales indicated that the UNSW Library did not currently carry the journal in which the Burks paper was published, Mathematical Tables and Other Aids to Computation; of course, the Library may have carried this journal in the past.)

Another computer, the American Burroughs B5000, announced in 1961 and delivered in 1963, also used a zero-address architecture, and also enabled reverse polish notation to be used for programming. R. S. Barton, one of the designers of the B5000, has written that he developed RPN independently of Hamblin, sometime in 1958 while reading a textbook on symbolic logic, and before he was aware of Hamblin’s work (Barton 1970). A decade after Hamblin first published his ideas, engineers at Hewlett-Packard (HP) developed a personal calculator, the 9100A Desktop Calculator, which used RPN. This calculator, the first in a long line by HP, was released in 1968, and it popularized RPN among the scientific and engineering communities; note, however, that early advertisements for the 9100A did not mention RPN.

Even if Hamblin’s work on RPN was not the first to be published that applied Polish Notation to a computational domain, people at the  time thought it was, as evidenced by the refereed publication of his 1962 paper in the Computer Journal, and Barton’s comments published in 1970.   Hamblin’s contribution to computer science was also recognized with an obituary in the Australian Computer Journal (Allen 1985) and in an influential history of British computing (Lavington 1980). In addition, GEORGE is listed in Bill Kinnersley’s comprehensive directory of computer languages, The Language List. In the 1960s, Hamblin also worked on implementing Tarski’s decision method for real closed fields (Tarski 1951), the first order theory of real numbers with addition and multiplication, and hired two programmers to assist in this project, Malcolm Newey and Vaughan Pratt. However, only in 1974 was it shown by Fischer and Rabin (1974) that the running time of this problem had an exponential lower bound.

Although usually not credited, Hamblin was the originator of two other ideas which subsequently became important in Artificial Intelligence. Firstly, Hamblin appears to have been the first person to define a formal measure of plausibility, distinct from that of probability, in a paper published in 1959. Alternative formalisms for uncertainty have come to play a very important role in Artificial Intelligence, particularly in the design of knowledge-based systems, due to the failure of the standard Kolmogorov axioms of probability to adequately account for all forms of uncertainty and for its manipulation. One person particularly taken by Hamblin’s work in this area was the British economist, George Shackle, who in the 1940s and 1950s had developed a theory of decision-making under uncertainty based on the potential surprise of rival uncertain beliefs, and focusing on the best-case and worst-case outcomes of alternative decision-options (see pp. 97 – 100 of Shackle 1969). (Shackle’s theory, based on his real-world experience of government economic policy making and business investment decisions, differed from the Maximum Expected Utility theory of Leonard J. Savage which has unfortunately come to dominate mainstream economics.)

Secondly, Hamblin was the first person to propose an axiomatic account of time based on intervals, rather than points. This was in a paper published in 1969. An interval calculus for time was later proposed by James Allen (1984), and has been influential in AI, both as a basis for reasoning about time, and, when extended to multiple dimensions, as a basis for reasoning about space (Anger and Rodriguez 1991).

From the 1960s, Hamblin returned to work in philosophy, particularly the philosophy of argumentation, and wrote two very influential books. One of these, Fallacies, published in 1970, is a study of the classical logical fallacies, such as begging the question, which Hamblin examined by means of formal dialogue games. These are games between speakers who utter statements according to strict rules, and they were first studied by Aristotle. Being rule-governed, these games have gained the attention of computer scientists, and, from about 1989, they have been applied in a number of areas, including: natural language processing; human-machine interaction; the design of complex software; and for dialogues between autonomous software agents (McBurney and Parsons 2009). Interaction and communication protocols based on formal dialogue games are likely to form the basis for the next generation of e-commerce systems and systems supporting high-level machine-to-machine communications. Another of Hamblin’s books, Imperatives, published posthumously in 1987, has also been influential in recent work in computer science, in modeling and implementing delegation of tasks between software agents (Atkinson et al. 2008, Reed and Norman 2007, Norman and Reed 2010).

Hamblin was fluent in several languages, including ancient Greek and Latin. He was one of three fellow-students from his time at Geelong Grammar to become professors of philosophy (the others being David Armstrong and Michael Scriven). At the time of his death, he was apparently attempting to set text of Wittgenstein to music.

Charles Hamblin was a pioneer computer scientist and a prominent philosopher, whose influence on the subject is still being felt. His contributions to applied and theoretical computing show the deep links which Computer Science has had, and continues to have, with philosophy and logic.

References:

In addition to the works cited in the text above, I have also listed all of Hamblin’s publications known to me.

J. F. Allen [1984]: Towards a general theory of action and time. Artificial Intelligence, 23(2): 123-154.

M. W. Allen [1985]: Charles Hamblin (1922-1985). The Australian Computer Journal, 17(4): 194-195.

F. D. Anger and R. V. Rodriguez [1991]: Time, tense, and relativity revisited. In: B. Bouchon-Meunier, R. R. Yager and L. A. Zadeh (Editors): Uncertainty in Knowledge Bases: Proceedings of the Third International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 1990), pp. 286-295. Heidelberg, Germany: Springer.

K. Atkinson, R. Girle, P. McBurney and S. Parsons [2008]: Command dialogues. In: I. Rahwan and P. Moraitis (Editors): Proceedings of the Fifth International Workshop in Argumentation in Multi-Agent Systems (ArgMAS 2008). AAMAS 2008, Lisbon, Portugal.

R. S. Barton [1970]: Ideas for computer systems organization: a personal survey. pp. 7-16 of: J. S. Jou (Editor): Software Engineering: Volume 1: Proceedings of the Third Symposium on Computer and Information Sciences held in Miami Beach, Florida, December 1969. New York, NY, USA: Academic Press.

R. C. Brigham and C. G. Bell [1959]: A Translation Routine for the DEUCE Computer. The Computer Journal, 2 (2): 76-84.

A. W. Burks, D. W. Warren and J. B. Wright [1954]: An analysis of a logical machine using paranthesis-free notation. Mathematical Tables and Other Aids to Computation, 8 (46): 53-57.

M. J. Fischer and M. O. Rabin [1974]: Super-exponential complexity of Pressburger arithmetic. Complexity of Computation, AMS-SIAM Proceedings. 7: 27-41.

R. J. Gillings and C. L. Hamblin [1964]: Babylonian reciprocal tables on UTECOM. Technology, 9 (2): 41-42, August 1964. An expanded version appeared in Australian Journal of Science, 27, 1964.

C. L. Hamblin [1957a]: Language and the Theory of Information. PhD Thesis, Logic and Scientific Method Programme, University of London, London, UK. Submitted October 1956, awarded 1957.

C. L. Hamblin [1957b]: An addressless coding scheme based on mathematical notation. Proceedings of the First Australian Conference on Computing and Data Processing, Salisbury, South Australia: Weapons Research Establishment, June 1957.

C. L. Hamblin [1957c]: Computer Languages. The Australian Journal of Science, 20: 135-139. Reprinted in The Australian Computer Journal, 17(4): 195-198 (November 1985).

C. L. Hamblin [1957d]: Review of: W. R. Ashby: Introduction to Cybernetics. Australasian Journal of Philosophy, 35.

C. L. Hamblin [1958a]: Questions. Australasian Journal of Philosophy, 36(3): 159-168.

C. L. Hamblin [1958b]: Review of: Time and Modality, by A. N. Prior. Australasian Journal of Philosophy, 36: 232-234.

C. L. Hamblin [1958c]: Surprises, innovations and probabilities. Proceedings of the ANU Symposium on Surprise, Canberra, July 1958.

C. L. Hamblin [1958d]: Review of: Formal Analysis of Normative Systems, by A. R. Anderson. Australasian Journal of Philosophy, 36.

C. L. Hamblin [1958e]: GEORGE Programming Manual. Duplicated, 1958. Revised and enlarged, 1959.

C. L. Hamblin [1959]: The Modal “Probably”. Mind, New Series, 68: 234-240.

C. L. Hamblin [1962]: Translation to and from Polish notation. Computer Journal, 5: 210-213.

C. L. Hamblin [1963]: Questions aren’t statements. Philosophy of Science, 30(1): 62-63.

C. L. Hamblin [1964a]: Has probability any foundations? Proceedings of the Symposium on Probability of the Statistical Society of New South Wales, May 1964. Reproduced in Science Yearbook, University of New South Wales, Sydney, 1964.

C. L. Hamblin [1964b]: Review of: Communication: A Logical Model, by D. Harrah. Australasian Journal of Philosophy, 42.

C. L. Hamblin [1964c]: Review of: Analysis of Questions, by N. D. Belnap.Australasian Journal of Philosophy, 42.

C. L. Hamblin [1965]: Review of: A Preface to the Logic of Science, by P. Alexander. The British Journal for the Philosophy of Science, 15(60): 360-362.

C. L. Hamblin [1966]: Elementary Formal Logic, a Programmed Course. (Sydney: Hicks Smith). Republished by Methuen, in London, UK, 1967. Also translated into Swedish by J. Mannerheim, under the title: Element”ar Logik, ein programmerad kurs. (Stockholm: Laromedelsf”orlagen, 1970).

C. L. Hamblin [1967a]: One-valued logic. Philosophical Quarterly, 17: 38-45.

C. L. Hamblin [1967b]: Questions, logic of. Encyclopedia of Philosophy. (New York: Collier Macmillan).

C. L. Hamblin [1967c]: An algorithm for polynomial operations. Computer Journal, 10.

C. L. Hamblin [1967d]: Review of: New Approaches to the Logical Theory of Interrogatives, by L. Aqvist. Australasian Journal of Philosophy, 44.

C. L. Hamblin [1969]: Starting and stopping. The Monist, 53: 410-425.

C. L. Hamblin [1970a]: Fallacies. London, UK: Methuen.

C. L. Hamblin [1970b]: The effect of when it’s said. Theoria, 36: 249-264.

C. L. Hamblin [1971a]: Mathematical models of dialogue. Theoria, 37: 130-155.

C. L. Hamblin [1971b]: Instants and intervals. Studium Generale, 24: 127-134.

C. L. Hamblin [1972a]: You and I. Analysis, 33: 1-4.

C. L. Hamblin [1972b]: Quandaries and the logic of rules. Journal of Philosophical Logic, 1: 74-85.

C. L. Hamblin [1973a]: Questions in Montague English. Foundations of Language, 10: 41-53.

C. L. Hamblin [1973b]: A felicitous fragment of the predicate calculus. Notre Dame Journal of Formal Logic. 14: 433-446.

C. L. Hamblin [1974]: La logica dell’iniziare e del cessare. Italian translation by C. Pizzi of an unpublished article: The logic of starting and stopping. Pages 295-317 in: C. Pizzi (Editor): La Logica del Tempo. Torino: Bringhieri.

C. L. Hamblin [1975a]: Creswell’s colleague TLM. Nous, 9(2): 205-210.

C. L. Hamblin [1975b]: Saccherian arguments and the self-application of logic. Australasian Journal of Philosophy, 53: 157-160.

C. L. Hamblin [1976]: An improved “Pons Asinorum”? Journal of the History of Philosophy, 14: 131-136.

C. L. Hamblin [1984]: Languages of Asia and the Pacific: A Phrasebook for Travellers and Students. (North Ryde, NSW: Angus and Robertson).

C. L. Hamblin [1987]: Imperatives. Oxford, UK: Basil Blackwell.

C. L. Hamblin and P. J. Staines [1992]: An extraordinarily simple theory of the syllogism. Logique et Analyse, 35: 81.

S. H. Lavington [1980]: Early British Computers: The Story of Vintage Computers and the People who Built Them. Manchester, UK: Manchester University Press.

P. McBurney and S. Parsons [2009]: Dialogue games for agent argumentation. Chapter 13 in: I. Rahwan and G. Simari (Editors): Argumentation in Artificial Intelligence. Berlin, Germany: Springer, pp. 261-280.

R. J. Nelson [1955]: Review of: “An analysis of a logical machine using paranthesis-free notation” by Arthur W. Burks, Don. W. Warren and Jesse B. Wright, The Journal of Symbolic Logic, 20 (1): 70-71.

T. J. Norman and C. Reed [2010]: A logic of delegation. Artificial Intelligence, 174 (1): 51-71.

T. Pearcey [1994]: Australian Computing: The Second Generation. Published in: J. M. Bennett, R. Broomham, P. M. Murton, T. Pearcey and R. W. Rutledge (Editors): Computing in Australia: The Development of a Profession. Australian Computer Society.

C. A. Reed. and T. J. Norman [2007]: A formal characterisation of Hamblin’s action-state semantics. Journal of Philosophical Logic, 36 (4): 415-448.

G. L. S. Shackle [1969]: Decision Order and Time in Human Affairs. Cambridge, UK: Cambridge University Press. Second Edition.

A. Tarski [1951]: A Decision Method for Elementary Algebra and Geometry. Berkeley, CA, USA: University of California Press.

R. A. Vowels [1978]: Introduction to PL/I, Algorithms and Structured Programming. Melbourne, 1978.

G. Williams [1985]: A shy blend of logic, maths and languages. (Obituary of Charles Hamblin). Sydney Morning Herald, 1985.

In compiling this biography, I am grateful for information and support received from: Gordon Bell, Jim Crosswhite, David Hitchcock, Jim Mackenzie, Vaughan Pratt, Michael Scriven, Phillip Staines, Robin Vowels, Doug Walton, and the family of the late Charles Hamblin. The views I express here are, of course, solely my own.

Copyright, 2003-2011.  All rights reserved.

One might imagine epistemology – the philosophy of knowledge – would be of help here.  Philosophers, however, have been seduced since Aristotle with propositions (factual statements about the world having truth values), largely ignoring actions, and their representation.   Philosophers of language have also mostly focused on speech acts – utterances which act to change the world – rather than on utterances about actions themselves.  Even among speech act theorists the obsession with propositions is strong, with attempts to analyze utterances which are demonstrably not propositions (eg, commands) by means of implicit assertive statements – propositions  asserting something about the world, where “the world” is extended to include internal mental states and intangible social relations between people – which these utterances allegedly imply.  With only a few exceptions (Thomas Reid 1788, Adolf Reinach 1913, Juergen Habermas 1981, Charles Hamblin 1987), philosophers of language have mostly ignored utterances  about actions.

Consider the following two statements:

I promise you to wash the car.

I command you to wash the car.

The two statements have almost identical English syntax.   Yet their meanings, and the intentions of their speakers, are very distinct.  For a start, the action of washing the car would be done by different people – the speaker and the hearer, respectively (assuming for the moment that the command is validly issued, and accepted).  Similarly, the power to retract or revoke the action of washing the car rests with different people – with the hearer (as the recipient of the promise) and the speaker (as the commander), respectively.

Linguists generally use “semantics” to refer to the real-world referants of syntactically-correct expressions, while “pragmatics” refers to other aspects of the meaning and use of an expression not related to their relationship (or not) to things in the world, such as the speaker’s intentions.  For neither of these two expressions does it make sense to speak of  their truth value:  a promise may be questioned as to its sincerity, or its feasibility, or its appropriateness, etc, but not its truth or falsity;  likewise, a command  may be questioned as to its legal validity, or its feasibility, or its morality, etc, but also not its truth or falsity.

For utterances about actions, such as promises, requests, entreaties and commands, truth-value semantics makes no sense.  Instead, we generally need to consider two pragmatic aspects.  The first is uptake, the acceptance of the utterance by the hearer (an aspect first identified by Reid and by Reinach), an acceptance which generally creates a social commitment to execute the action described in the utterance by one or other party to the conversation (speaker or hearer).    Once uptaken, a second pragmatic aspect comes into play:  the power to revoke or retract the social commitment to execute the action.  This revocation power does not necessarily lie with the original speaker; only the recipient of a promise may cancel it, for example, and not the original promiser.  The revocation power also does not necessarily lie with the uptaker, as commands readily indicate.

Why would a computer scientist be interested in such humanistic arcana?  The more tasks we delegate to intelligent machines, the more they need to co-ordinate actions with others of like kind.  Such co-ordination requires conversations comprising utterances over actions, and, for success, these require agreed syntax, semantics and pragmatics.  To give just one example:  the use of intelligent devices by soldiers have made the modern battlefield a place of overwhelming information collection, analysis and communication.  Lots of this communication can be done by intelligent software agents, which is why the US military, inter alia, sponsors research applying the philosophy of language and the  philosophy of argumentation to machine communications.

Meanwhile, the philistine British Government intends to cease funding tertiary education in the arts and the humanities.   Even utilitarians should object to this.

References:

Juergen  Habermas [1984/1981]:   The Theory of Communicative Action:  Volume 1:  Reason and the Rationalization of Society.  London, UK:  Heinemann.   (Translation by T. McCarthy of:  Theorie des Kommunikativen Handelns, Band I,  Handlungsrationalitat und gesellschaftliche Rationalisierung. Suhrkamp, Frankfurt, Germany, 1981.)

Charles  L. Hamblin [1987]:  Imperatives. Oxford, UK:  Basil Blackwell.

P. McBurney and S. Parsons [2007]: Retraction and revocation in agent deliberation dialogs. Argumentation, 21 (3): 269-289.

Adolph Reinach [1913]:  Die apriorischen Grundlagen des bürgerlichen Rechtes.  Jahrbuch für Philosophie und phänomenologische Forschung, 1: 685-847.

It took until 2006 and the development of x-ray tomography for a plausible theory of the purpose and operations of the Antikythera Mechanism to be proposed (Freeth et al. 2006).   The machine was said to be a physical examplification of  late Greek theories of cosmology, in particular the idea that the motion of a heavenly body could  be modeled by an epicycle – ie, a body traveling around a circle, which is itself moving around some second circle.  This model provided an explanation for the fact that many heavenly bodies appear to move at different speeds at different times of the year, and sometimes even (appear to) move backwards.

There have been two recent developments:  One is the re-creation of the machine (or, rather, an interpretation of it)  using lego components.

The second has arisen from a more careful examination of the details of the mechanism.  According to Marchant (2010), some people now believe that the mechanism examplifies Babylonian, rather than Greek, cosmology.   Babylonian astronomers modeled the movements of heavenly bodies by assuming each body traveled along just one circle, but at two different speeds:  movement in one period of the year being faster than during the other part of the year.

If this second interpretation of the Antikythera Mechanism is correct, then perhaps it was the mechanism itself (or others like it) which gave late Greek astronomers the idea for an epicycle model.   In support of this view is the fact that, apparently, gearing mechanisms and the epicycle model both appeared around the same time, with gears perhaps a little earlier.   So late Greek cosmology (and perhaps late geometry) may have arisen in response to, or at least alongside, practical developments and physical models.   New ideas in computing typically follow the same trajectory – first they exist in real, human-engineered, systems; then, we develop a formal, mathematical theory of them.   Programmable machines, for instance, were invented in the textile industry in the first decade of the 19th century (eg, the Jacquard Loom), but a mathematical theory of programming did not appear until the 1960s.   Likewise, we have had a fully-functioning, scalable, global network enabling multiple, asynchronous, parallel, sequential and interleaved interactions since Arpanet four decades ago, but we still lack a thorough mathematical theory of interaction.

And what have the Babylonians ever done for us?   Apart from giving us our units for measuring of time (divided into 60) and of angles (into 360 degrees)?

References:

T Freeth, Y Bitsakis, X Moussas, JH Seiradaki, A Tselikas, H Mangou, M Zafeiropoulou, R Hadland, D Bate, A Ramsey, M Allen, A Crawley, P Hockley, T Malzbender, D Gelb,W Ambrisco and MG Edmunds [2006]:  Decoding the ancient Greek astronomical calculator known as the Antikythera Mechanism.  Nature,  444 (30):   587-591.  30 November 2006.

J. Marchant [2010]:  Mechanical inspiration.  Nature, 468:  496-498.  25 November 2010.

Sport is international phatic but also a crucial Australian (male) vehicle. It enables not just short, passing greetings but allows for what may seem like deep, passionate and meaningful conversations but which in the end are unmemorable, empty, producing nothing and enhancing no one.

Unmemorable?! Really?   What Australian could forget Norman May’s shouted “Gold! Gold for Australia! Gold!” commentary at the end of the men’s 400-metre swimming medley at the 1980 Olympics in Moscow.  Only a churlish gradgrind could fail to be enhanced by hearing this.   And what Australian of a certain age could forget the inimitable footie commentary of Rex Mossop, including, for example, such statements as,  “That’s the second consecutive time he’s done that in a row one straight after the other.” Mossop’s heat-of-the-moment sporting talk was commemorated with his many winning places in playwright Alex Buzo’s Australian Indoor Tautology Pennant, an annual competition held, as I recall,  in Wagga Wagga, Gin Gin and Woy Woy (although not in Woop Woop or in The Never Never), before moving internationally to exotic locations such as Pago Pago, Xai Xai and Baden Baden.  Unmemorable, Mr Tatz?  Enhancing no one?  Really?  To be clear, these are not memorable sporting events, but memorable sporting commentary.   And all I’ve mentioned so far is sporting talk, not the great writers on baseball, on golf, on cricket, on swimming,  . . .

But as well as misunderstanding what talk about sport is about and why it is meaningful, Tatz is wrong on another score.   He says:

But why so much natter and clatter about sport? Eco’s answer is that sport “is the maximum aberration of ‘phatic’ speech”, which is really a negation of speech.

Phatic speech is meaningless speech, as in “G’day, how’s it going?” or “have a nice day” or “catch you later” — small talk phrases intended to produce a sense of sociability, sometimes uttered in the hope that it will lead to further and more real intercourse, but human enough even if the converse goes no further.

Phatic communications are about establishing and maintaining relationships between people.  Such a purpose is the very essence of speech communication, not its negation.  Tatz, I fear, has fallen into the trap of so many computer scientists – to focus on the syntax of messages, and completely ignore their semantics and pragmatics.    The syntax of messages concerns their surface form, their logical structure, their obedience (or not) to rules which determine whether they are legal and well-formed statements (or not) in the language they purport to arise from.  The semantics of utterances concerns their truth or falsity, in so far they describe real objects in some world (perhaps the one we all live in, or some past, future or imagined world),  while their pragmatics concerns those aspects of their meaning unrelated to their truth status (for example, who has power to revoke or retract them).

I have discussed this syntax-is-all-there-is mistake before.    I believe the root causes of this mistaken view are two-fold: the mis-guided focus of philosophers these last two centuries on propositions to the exclusion of other types of utterances and statements (of which profound error Terry Eagleton has shown himself guilty), and the mis-guided view that we now live in some form of Information Society, a view which wrongly focuses attention on the information  transferred by utterances to the exclusion of any other functions that utterances may serve or any other things we agents (people and machines) may be doing and aiming to do when we talk.   If you don’t believe me about the potentially complex functionality of utterances, even when viewed as nothing more than the communication of factual propositions, then read this simple example.

If communications were only about the transfer of explicit information, then life would be immensely less interesting.  It would also not be human life, for we would be no more intelligent than desktop computers passing HTTP requests and responses to one another.

The Internet, the World-Wide-Web and hypertext were all forecast by Vannevar Bush, in a July 1945 article for The Atlantic, entitled  As We May Think.  Perhaps this is not completely surprising since Bush had a strong influence on WW II and post-war military-industrial technology policy, as Director of the US Government Office of Scientific Research and Development.  Because of his influence, his forecasts may to some extent have been self-fulfilling.

However, his article also predicted automated machine reasoning using both logic programming, the computational use of formal logic, and computational argumentation, the formal representation and manipulation of arguments.  These areas are both now important domains of AI and computer science which developed first in Europe and which still much stronger there than in the USA.   An excerpt:

The scientist, however, is not the only person who manipulates data and examines the world about him by the use of logical processes, although he sometimes preserves this appearance by adopting into the fold anyone who becomes logical, much in the manner in which a British labor leader is elevated to knighthood. Whenever logical processes of thought are employed—that is, whenever thought for a time runs along an accepted groove—there is an opportunity for the machine. Formal logic used to be a keen instrument in the hands of the teacher in his trying of students’ souls. It is readily possible to construct a machine which will manipulate premises in accordance with formal logic, simply by the clever use of relay circuits. Put a set of premises into such a device and turn the crank, and it will readily pass out conclusion after conclusion, all in accordance with logical law, and with no more slips than would be expected of a keyboard adding machine.

Logic can become enormously difficult, and it would undoubtedly be well to produce more assurance in its use. The machines for higher analysis have usually been equation solvers. Ideas are beginning to appear for equation transformers, which will rearrange the relationship expressed by an equation in accordance with strict and rather advanced logic. Progress is inhibited by the exceedingly crude way in which mathematicians express their relationships. They employ a symbolism which grew like Topsy and has little consistency; a strange fact in that most logical field.

A new symbolism, probably positional, must apparently precede the reduction of mathematical transformations to machine processes. Then, on beyond the strict logic of the mathematician, lies the application of logic in everyday affairs. We may some day click off arguments on a machine with the same assurance that we now enter sales on a cash register. But the machine of logic will not look like a cash register, even of the streamlined model.”

Edinburgh sociologist, Donald MacKenzie, wrote a nice history and sociology of logic programming and the use of logic of computer science, Mechanizing Proof: Computing, Risk, and Trust.  The only flaw of this fascinating book is an apparent misunderstanding throughout that theorem-proving by machines  refers only to proving (or not) of theorems in mathematics.    Rather, theorem-proving in AI refers to proving claims in any domain of knowledge represented by a formal, logical language.    Medical expert systems, for example, may use theorem-proving techniques to infer the presence of a particular disease in a patient; the claims being proved (or not) are theorems of the formal language representing the domain, not necessarily mathematical theorems.

References:

Donald MacKenzie [2001]:  Mechanizing Proof: Computing, Risk, and Trust (2001).  Cambridge, MA, USA:  MIT Press.

Vannevar Bush [1945]:  As we may think.  The Atlantic, July 1945.

Technorati Tags: logic programming, computational argumentation, Donald MacKenzie, Vannevar Bush

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully ‘crowd-sourced’ through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.”

References:

Seth Cooper et al. [2010]: Predicting protein structures with a multiplayer online game.  Nature, 466:  756–760.  Published:  2010-08-05.

Eric Hand [2010]:  Citizen science:  people power.  Nature 466, 685-687. Published 2010-08-04.

The Foldit game is here.

Technorati Tags: crowd-sourced, Protein structure prediction

For some time, I have been writing on these pages that the currently-fashionable paradigm of the Information Society is inadequate to describe what most of us do at work and play, or to describe how computing technologies support those activities (see, for example, recently here, with a collection of posts here).   Most work for most people in the developed world is about coordinating their actions with those of others  – colleagues, partners, underlings, bosses, customers, distributors, suppliers, publicists, regulators, und so weiter.   Information collection and transfer, while often important and sometimes essential to the co-ordination of actions,  is not usually itself the main game.

Given the extent to which computing technologies already support and enable human activities (landing our large aircraft automatically when there is fog, for example), the InfoSoc paradigm, although it may describe well the transmission of zeros and ones between machines, is of little value in understanding what these transmissions mean.  Indeed, the ur-text of the Information Society, Shannon’s mathematical theory of communications (Shannon 1948) explicitly ignores the semantics of messages!  In place of the InfoSoc metaphor, we need another new paradigm, a new way to construe what we are all doing.  For now, let me call it the Joint-Action Society, although this does not quite capture all that is intended.

I am pleased to learn that I am not alone in my views about InfoSoc.   I recently came across an article by the late Peter Martin, journalist, editor and e-businessman, about the lessons from that great disaster of privatization of Railtrack in the UK.  (In the 1980s and 90s, the French had grand projets while the British had great project management disasters.)  Here is Martin, writing in the FT in October 2001 (the article does not seem to be available online):

Railtrack had about a dozen prime contractors, which in turn farmed out the work to about 2,000 subcontractors.  Getting this web of relationships to work was a daunting task.  Gaps in communication, and the consequent “blame culture” are thought to be important causes of the track problems that led to the Hatfield crash which undermined Railtrack’s credibility.

.  .  .

These practical advantages of wholesale outsourcing rely, however, on unexamined assumptions.  It is these that the Railtrack episode comprehensively demolishes.

The first belief holds that properly specified contracts can replicate the operations of an integrated business.  Indeed, on this view, they may be better than integration because everyone understands what their responsibilities are, and their  incentives are clear and tightly defined.

This approach had a particular appeal to governments, as they attempted to step back from the minutiae of delivering public services.  British Conservative governments used the approach to break up monolithic nationalised industries into individual entities, such as power generators and distributors.

They put this approach into effect at the top level of the railway system by splitting the task of running the track and the signalling (Railtrack’s job) from the role of operating the trains.  It is not surprising that Railtrack, born into this environment, carried the approach to its logical conclusion in its internal operation.

.  .  .

In 1937, the Nobel prize-winning economist Ronald Coase had explained that companies perform internally those tasks for which the transactional costs of outsourcing are too high.

What fuelled the outsourcing boom of the 1990s was the second unexamined assumption – that the cost of negotiating, monitoring and maintaining contractual relationships with outsourcing partners had dropped sharply, thanks to the revolution in electronic communications.  The management of a much bigger web of contractors – indeed, the creation of a “virtual company” – became feasible.

In practice, of course, the real costs of establishing and maintaining contracts are not those of information exchange but of establishing trust, alignment of interests and a common purpose.  Speedy and cheap electronic communications have only a minor role to play in this process, as Coase himself pointed out in 1997.

.   .   .

And perhaps that is the most useful lesson from the Railtrack story: it is essential to decide what tasks are vital to your corporate purpose and to devote serious resources to achieving them.   Maintaining thousands of miles of steel tracks and stone chippings may be a dull, 19th century kind of task.   But as Railtrack found, if you can’t keep the railway running safely, you haven’t got a business.”

Reference:

Peter Martin [2001]: Lessons from Railtrack.  The collapse has demolished some untested assumptions about outsourcing.  Financial Times, 2001-10-09, page 21.

Claude E. Shannon [1948/1963]: The mathematical theory of communication. Bell System Technical Journal, October and November 1948.  Reprinted in:  C. E. Shannon and W. Weaver [1963]: The Mathematical Theory of Communication. pp. 29-125. Chicago, IL, USA: University of Illinois Press.

Technorati Tags: Information Society

A recent issue of ACM Transactions on Computer Systems (ACM TOCS) carries a paper with a wonderful example of this principle.  Faced with a denial-of-service attack, they propose that a server ask all its clients to increase their messages to the server.  Most likely, attackers among the clients are already transmitting at their local full capacity, and so are unable to do this, which means that messages from attackers will form a decreasing proportion of all messages received by the server.   The paper abstract is:

This article presents the design, implementation, analysis, and experimental evaluation of speak-up, a defense against application-level distributed denial-of-service (DDoS), in which attackers cripple a server by sending legitimate-looking requests that consume computational resources (e.g., CPU cycles, disk). With speak-up, a victimized server encourages all clients, resources permitting, to automatically send higher volumes of traffic. We suppose that attackers are already using most of their upload bandwidth so cannot react to the encouragement. Good clients, however, have spare upload bandwidth so can react to the encouragement with drastically higher volumes of traffic. The intended outcome of this traffic inflation is that the good clients crowd out the bad ones, thereby capturing a much larger fraction of the server’s resources than before. We experiment under various conditions and find that speak-up causes the server to spend resources on a group of clients in rough proportion to their aggregate upload bandwidths, which is the intended result.

Reference:

Michael Walfish, Mythili Vukurutu, Hari Balakrishnan, David Karger and Scott Shenker [2010]:  DDoS defense by offense.  ACM Transactions on Computer Systems, 28 (1), article 3.