The algorithmic top-down view of causation in financial markets is essentially a deterministic, dynamical systems view. This can serve as an interpretation of financial markets whereby markets are understood through assets prices, representing information in the market, which can be described by a dynamical system model. This is the ideal encapsulated in the Laplacian vision:
We ought to regard the present state of the universe as the effect of its antecedent state and as the cause of the state that is to follow. An intelligence knowing all the forces acting in nature at a given instant, as well as the momentary positions of all things in the universe, would be able to comprehend in one single formula the motions of the largest bodies as well as the lightest atoms in the world, provided that its intellect were sufficiently powerful to subject all data to analysis; to it nothing would be uncertain, the future as well as the past would be present to its eyes. The perfection that the human mind has been able to give to astronomy affords but a feeble outline of such an intelligence.
Here boundary and initial conditions of variables uniquely determine the outcome for the effective dynamics at the level in hierarchy where it is being applied. This implies that higher levels in the hierarchy can drive broad macro-economic behavior, for example: at the highest level there could exist some set of differential equations that describe the behavior of adjustable quantities, such as interest rates, and how they impact measurable quantities such as gross domestic product, aggregate consumption.
The literature on the Lucas critique addresses limitations of this approach. Nevertheless, from a completely ad hoc perspective, a dynamical systems model may offer a best approximation to relationships at a particular level in a complex hierarchy.
Predictors: This system actor views causation in terms of uniquely determined outcomes, based on known boundary and initial conditions. Predictors may be successful when mechanistic dependencies in economic realities become pervasive or dominant. An example of a predictive-based argument since the Global Financial Crises (2007-2009) is the bipolar Risk- On/Risk-Off description for preferences, whereby investors shift to higher risk portfolios when global assessment of riskiness is established to be low and shift to low risk portfolios when global riskiness is considered to be high. Mathematically, a simple approximation of the dynamics can be described by a Lotka-Volterra (or predator-prey) model, which in economics, proposed a way to model the dynamics of various industries by introducing trophic functions between various sectors, and ignoring smaller sectors by considering the interactions of only two industrial sectors. The excess-liquidity due to quantitative easing and the prevalence and ease of trading in exchange traded funds and currencies, combined with low interest rates and the increase use of automation, pro- vided a basis for the risk-on/risk-off analogy for analysing large capital flows in the global arena. In Ising-Potts hierarchy, top down causation is filtered down to the rest of the market through all the shared risk factors, and the top-down information variables, which dominate bottom-up information variables. At higher levels, bottom-up variables are effectively noise terms. Nevertheless, the behaviour of the traders in a lower levels can still become driven by correlations across assets, based on perceived global riskiness. Thus, risk-on/risk-off transitions can have amplified effects.