Embedding Quantum into Classical: Contextualization vs Conditionalization

We compare two approaches to embedding joint distributions of random variables recorded under different conditions (such as spins of entangled particles for different settings) into the framework of classical, Kolmogorovian probability theory. In the contextualization approach each random variable is "automatically" labeled by all conditions under which it is recorded, and the random variables across a set of mutually exclusive conditions are probabilistically coupled (imposed a joint distribution upon). Analysis of all possible probabilistic couplings for a given set of random variables allows one to characterize various relations between their separate distributions (such as Bell-type inequalities or quantum-mechanical constraints). In the conditionalization approach one considers the conditions under which the random variables are recorded as if they were values of another random variable, so that the observed distributions are interpreted as conditional ones. This approach is uninformative with respect to relations between the distributions observed under different conditions, because any set of such distributions is compatible with any distribution assigned to the conditions.Comment: PLoS One 9(3): e92818 (2014

Exact limiting solutions for certain deterministic traffic rules

We analyze the steady-state flow as a function of the initial density for a class of deterministic cellular automata rules (``traffic rules'') with periodic boundary conditions [H. Fuks and N. Boccara, Int. J. Mod. Phys. C 9, 1 (1998)]. We are able to predict from simple considerations the observed, unexpected cutoff of the average flow at unity. We also present an efficient algorithm for determining the exact final flow from a given finite initial state. We analyze the behavior of this algorithm in the infinite limit to obtain for R_m,k an exact polynomial equation maximally of 2(m+k)th degree in the flow and density.Comment: 25 pages, 8 figure

Conversations on Contextuality

In the form of a dialogue (imitating in style Lakatos's Proof and Refutation), this chapter presents and explains the main points of the approach to contextuality dubbed Contextuality-by-Default.Comment: Opening chapter (pp. 1-22) in "Contextuality from Quantum Physics to Psychology," edited by E. Dzhafarov, S. Jordan, R. Zhang, V. Cervantes. New Jersey: World Scientific Press, 201

Climate change impacts on crop risks and agricultural production in Finland

Climate Change is expected to improve crop production conditions significantly in northern Europe. In some studies a significant increase in agricultural production is expected in northern Europe while drought is becoming gradually more severe in Southern Europe and France due to climate change. Our analysis provides one of the first steps in evaluating the possible scale of changes in agricultural production and use of farm land in Finland, so far one of the least favoured agricultural areas in Europe. Drought in early summer and low temperature sum during growing period are currently the main growth limiting factors in Finland. Even though the early part of the growing period is likely to be dry also in the future, both mean and variance of crop yields are expected to increase in Finland. However the changes are likely to be very different for different crops. We use mean-variance analysis and a sector level, regionally disaggregated, optimisation-based economic model in evaluating the likely impacts increasing mean and variance of crop yields on agricultural production and land use in Finland. Our sector level analysis takes into account important supply-demand conditions, e.g. changes in feed crop use, limited domestic demand, and imperfect substitution. Sector models including risk are relatively few in recent literature of agricultural economics. In our first analysis we use mean-variance approach in including crop yield risk and risk aversion explicitly in our dynamic recursive sector model. Increasing mean and variability of crop yields change the relative profitability of crops. For example, increasing feed crop yields may drive up animal production, especially if export prices of meat remain strong. On the other hand, if the demand is relatively inelastic, land area of some less competitive crops may clearly decrease despite increasing yields. Consequently, climate change may trigger changes on the production structure of agriculture. Farm income is relatively little affected by higher crop yields, on the average, while income may increase significantly on some individual farms. Our preliminary analysis also shows that policy measures aimed at reducing crop risks may be relatively efficient in increasing or sustaining production of many crops. Even if policy measures for reducing risks are commonly perceived less production-linked compared to price and area based subsidies, we show that their impact on production volume will most likely increase in on-going climate change. However in further research we need to test alternative approaches for better representation and modelling of crop risks. Albeit simple, we consider optimisation and risk aversion necessary techniques and assumptions in producing consistent evaluations of economic behaviour in long-term economic adaptations and risks.Climate change, agriculture, risk aversion, mean-variance analysis, sector models, optimization, crop production, Finland, Crop Production/Industries, Environmental Economics and Policy, Risk and Uncertainty,