by Chandre Dharmawardana, Ottawa, Canada

I wish to comment on an interesting article by Leelanada De Silva (Sunday Island of Jan. 28), from a scientistƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢s perspective.ƒÆ’-¡ƒ”š‚ Articles by Usvatta-aratchi and Carlo Fonseka (The Island Oct. 19), and by Gunadasa Amarasekera et al treated related topics. When my article entitled `Some lessons from ChinaƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ (The Island of Jan. 12 Jan.) appeared some readers wrote that `planning is better than blind processƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢. Leelananda suggests that effective planning is possible in both socialist and capitalist systems.

Social andƒÆ’-¡ƒ”š‚ physical sciences

Planners need to predict and choose among various possibilities. The task of scientists is also prediction. The subject known as `man-body theoryƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ dealsƒÆ’-¡ƒ”š‚ with the statistical mechanics of many agents influencing one another. Hence, mathematical physicists find mathematical economics to be familiar reading. Indeed, the success of quantitative models in science prompted quantitative approaches to economics. Samuelson, von Neumann, Morgenstern et al are names familiar to mathematical physicistsƒÆ’-¡ƒ”š‚ and economists alike. Thus, began the rise ofƒÆ’-¡ƒ”š‚  `econometricsƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ and model building, using elementary dynamical and statistical concepts like time-series analysis, bell curves, Z-scores, regression, input-output matrices, Markov and Non-Markov processes, game theory, Hilbert spaces, etc.

However, in physics, theories have to make successful predictions of controlled experiments before they are used. Controlled experiments are impossible in the social sciences even for dictators.ƒÆ’-¡ƒ”š‚  Nevertheless, ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”economic planningƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ as an extension of budgeting and fiscal policy soon became the norm.ƒÆ’-¡ƒ”š‚  The centralized ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”socialistƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ economies seemed best forƒÆ’-¡ƒ”š‚ planning optimal growth, but risky for individual liberties.ƒÆ’-¡ƒ”š‚ Economists and bankers began to dictate what governments ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”should doƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢, while collecting fat bonuses and failing to predict financial crises staringƒÆ’-¡ƒ”š‚ in their faces.

When Carlo Fonseka says that ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”Cuban communists produce 500 doctors while a comparable Capitalist societyƒÆ’-¡ƒ”š‚ produces only 50ƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢, he applaudsƒÆ’-¡ƒ”š‚ centralized ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”socialistƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ planning. However, the 500 doctors find shortagesƒÆ’-¡ƒ”š‚ of medicine in the ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”planned economyƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ and the patients die. In contrast, drugs are plentiful in the capitalist economy and doctors prescribe lavishly to enrich the companies and clinics. The over-medicated patients become permanent cash cows.

Scepticism about predictability

Eclipses could be foretold with uncanny accuracy andƒÆ’-¡ƒ”š‚ celestial mechanics is the paradigm of predictability. However, the great 19th-centuryƒÆ’-¡ƒ”š‚ mathematician, Henri Poincare, proved that some simple clock-work like systems as well as all complex systems were beyond prediction. Practical indeterminism has risen out of formal determinism.ƒÆ’-¡ƒ”š‚ Historical evolution is not the dialectical doing of big forces, but a devil dancing on details.

Physicists and chemists also realized the importance of Poincare. Science is successful because itƒÆ’-¡ƒ”š‚ uses a ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”reductionistƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ approach limiting a system to a few variables. Although we can precisely predict isolated quantum systems with even a thousand or so particles, we cannot predict the exact conformation of a protein at room temperature. Complex systems are governed by differential equations having the indeterminacies discovered by Poincare a dozen decades ago.

Statistically, virtually all swans should be white. Social theorists began to realize that social planning has to deal with ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”black-swansƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ -ƒÆ’-¡ƒ”š‚ Nicholas TalebƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢s terminology for the occurrence of the utterly unanticipated.ƒÆ’-¡ƒ”š‚ ƒÆ’-¡ƒ”š‚  Chaotic systemsƒÆ’-¡ƒ”š‚ do not obey normal distribution, Z-scores, extrapolations from regression analysis, Markov chains etc. They are full of black swans, intensely sensitive to initial conditions and defy prediction.

Complex systems

Physicists and mathematicians wereƒÆ’-¡ƒ”š‚ humbled when Yakov Sinai proved in the 1960s that a class of billiard problems could be unpredictable and chaotic! So the scientist trying to predict the behaviour of a bacterium with millions of interacting proteins needs new tools. Such tools can be tested by experiments. Fund managers and economists also have to make predictions. But they cannot openly do experiments.

Nature solves the problem of finding the best seeds by scattering a million seeds and allowingƒÆ’-¡ƒ”š‚ Darwinian ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”co-opetitionƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ (such co-opetition often involves profitable cooperation and competition). ScientistsƒÆ’-¡ƒ”š‚ model this usingƒÆ’-¡ƒ”š‚ `Monte-CarloƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ type computer simulations. By analogy, capitalist societies allowingƒÆ’-¡ƒ”š‚ Darwinian co-opetition are more likely toƒÆ’-¡ƒ”š‚ succeed. This was what Deng Xiaoping realized in 1977, in fixing a failed Maoist China.

Such simulations have to be constrained by limiting the ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”allowed movesƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ to energy controlled ƒÆ’‚¢ƒ¢-¡‚¬ƒ”¹…”Metropolis-TellerƒÆ’‚¢ƒ¢-¡‚¬ƒ¢-¾‚¢ moves. By analogy, we need controlled economies with capitalism balanced by social checks, liberty balanced by authority, human-rights moderated by social rights, and so on. Many competing plans and planners (many board rooms!) are needed for the task.

Social experiments – revolutionƒÆ’-¡ƒ”š‚ or devolution – are full of dangerous black swans. Social planners should continue to study societies, butƒÆ’-¡ƒ”š‚ avoid any centralized plan implementation.