Jan Adriaans

                                          Bio / Resume


 Selection of works







         The Geography of Crisis

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                 Synchrony Collapse

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              Las Vegas' Mythology

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A big proportions of bank decisions are automated through decision models (by set of rules like statistical algorithms). The aim is to detect the emergence of future crashes and respond appropriately, in order to prevent. Through a blend of mathematics, statistics and computing, the behaviour of financial institutions is measured and predicted.

   Financialization, a process by which financial institutions and markets increase in size and influence, is pushing the economy further into abstraction. This economic system gives people the appearance of a quantifiable and measurable economy, put in motion and controlled by logic through the rational compression of reality. The model creates an understanding of the correlation of economic forces, it helps politicians to make policy and companies to answer for their strategies. Is it possible for the prediction model to measure every turbulence of action? What are the consequences of economic modelling? And why is the immediate response to a crisis a reactionary response instead of a moment of change?

   Economic journalist John Cassidy claims that the root problem with economic models is conceptual. “The financial market isn’t a deterministic system underpinned by laws of nature, and attempts to treat it like one—such as contemporary risk-management techniques—are destined to backfire.”

   The process of identifying, quantifying, and, if desired, reducing financial risk is to keep financial operators act within certain margins. It would prevent actors from producing bubbles, when the price of an asset in trading strongly exceeds the asset’s intrinsic value. A movement which can accelerate a price downfall is the impulsive response of individual operators slipping in sync with their co-operators, when they lose their faith in the market, creating a very powerful motion when many actors become one. This dynamic is similar to the flocking of birds. Nobody knows who’s leading the group and who’s choosing the direction, yet automatically or by instinct everyone follows. In those moments, there’s no-one who wants to stay behind and get separated from the group. People show unpredictable behavior under pressure, and individual action can become a homogenous force when a whole group of individuals are being sucked into this void. When one market crashes it can easily endanger other markets as well. The question if its contingent human behavior deviating from a risk model what is putting the economy in danger? Or do we have to look deeper into the economic model itself?

   Economy is shaped by what monitors it, and this shapes the way we set the model. It is a positive feedback loop. The model is not set in a context it creates context. We can’t distinguish ourselves from the model that predicts, and which demands a certain strategy.

   Football players are trained to act in formation and respond according to certain strategies and models. A team player can act outside the of the model but the outcome will be more uncertain. This can lead to a bigger gain but also a higher loss. The player could be tricked by an opponent knowing that his impulsive response could give the opponent a chance to take advantage. The internalization of a model is a premise to play, strategies should be run by instinct, but depart in a rational understanding of the game. We all know how team of small children is running towards the ball without sense of position and formation. Training is necessary to keep your position and act according to tactics, to respond automatically determined by reason.  This area is almost completely contained, a closed in playground, with no goals, no field-lines of yard-lines, not even an audience. The effect of their action doesn’t move beyond the boundaries of the campus. The players train and play a non-official game, just for the sake of the pleasure of the game itself.

   The Stock market in Shanghai which was re-established after 40 years of absence in 1990, was seriously tumbling in July 2015. The Chinese market is mostly consisting of individual investors, who had been acting similarly because of a “panic sentiment.” Primary, investors were seduced to buy because of low interest rates, which resulted in a momentum of overinvestment, not substantiated by a real foundation. The Chinese stock market was rising fast. At a time when the Chinese economy was losing energy, the stocks were looking wildly overvalued. As fears grew that the rise in many stocks was unsustainable, the selling started. The tumbling market effected the trust in other stock markets in surrounding countries, when an increasing amount of investors sold their Chinese stocks. These aftershocks can become bigger than the initial shock. Because so many of the investors in the market are individuals, the government will be acutely aware that deepening losses risk denting the real economy and even fueling social unrest.

   In reverse if the real economy is flourishing, shareholders might respond anxiously as well. If people buy more products it could drive on inflation (prizes). And if prizes go up, the interest of the National Bank has to go up as well. If the interest rises, obligations become more attractive then shares. Because obligations are unsecured loans that thrive under high interest rates. Fearing this scenario, shareholders start to sell their shares and the rates will drop fast.

   There’s a supposed duality of the model based on rules, and the irrational behavior of individuals suddenly acting as a homogenous group, in moments of anxiety. It makes the ideology of capitalism feel like something naturally deterministic, developing like a stream coming down the mountain turning into a flood. But in fact it is people behaving contingent and often irrational within or because of the designed model of operation.

   According to Marta Poon “We do not face financial risk, we make financial risk within controlled environments.”  With the measurement of risk, risk became a commodity, a product for trading and speculation. Arjun Appadurai argues: “The machinery for measuring, modeling, managing, predicting, commoditizing, and exploiting risk has become the central diacritic of modern capitalism. Financial markets lead and shape other markets, financial capital vastly outstrips manufacturing or industrial capital, financial policy makers dominate global economic policy, and major economic crises are produced and prolonged by the runaway growth of risk instruments, markets, and creative legal and accounting devices.”

   The future crisis we face is not a product of a misuse of the model, it could be brought back to model itself. Therefor we could say that also the word crisis is up for a reinterpretation. The etymology of the term “crisis” is said to originate in the ancient Greek term krinô. By its meaning: to separate, to choose, cut to decide, to judge it becomes clear krinô is a critical moment which needs decisive action. The impact of the term crisis, is confirmed in the fact that around that time, besides for law and theology, it was mainly used within medical grammar. Crisis pointed out a critical phase of a disease, a condition where the patient could die or recover. This phase called for decision making, and a sharp judgement between alternatives. Crisis wasn’t the disease or illness itself, but it described that particular singular moment, where indecisiveness would be fatal. Today the term crisis has become the long-term disease, an ongoing condition where alternatives no longer become optional.

   For what reason evaluated the public opinion and political response to a moment of crisis into something increasingly reactionary, aversive to change and experiment? For Roitman, the misunderstanding lies in the fact that we use an imaginary departure point as a solid point of reference, as a truth or righteous way we should reset to. To think crisis as a human misuse of an ideal model, or alienation from a perfect situation, we overlook the fact that these models are already part of our judgement, and build with biases all along. “A bank run is not due to an error of the model, but is rather due to the execution of the model in practice.”


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