55,983 research outputs found
Structural measures for games and process control in the branch learning model
Process control problems can be modeled as closed recursive games.
Learning strategies for such games is equivalent to the concept of
learning infinite recursive branches for recursive trees. We use this
branch learning model to measure the difficulty of learning and
synthesizing process controllers. We also measure the difference
between several process learning criteria, and their difference to
controller synthesis. As measure we use the information content
(i.e. the Turing degree) of the oracle which a machine need to get the
desired power.
The investigated learning criteria are finite, EX-, BC-, Weak BC- and
online learning. Finite, EX- and BC-style learning are well known from
inductive inference, while weak BC- and online learning came up with
the new notion of branch (i.e. process) learning. For all considered
criteria - including synthesis - we also solve the questions of their
trivial degrees, their omniscient degrees and with some restrictions
their inference degrees. While most of the results about finite, EX-
and BC-style branch learning can be derived from inductive inference,
new techniques had to be developed for online learning, weak BC-style
learning and synthesis, and for the comparisons of all process
learning criteria with the power of controller synthesis
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Boundedly rational versus optimization-based models of strategic thinking and learning in games
The paper is a comment on the article by R. Harstad and R. Selten and considers the tradeoff between bounded rationality and optimization models in the game-theoretic context. The author shows that in most of the models elements of opimization are still retained and that it is thus more productive to further improve the optimization-based modeling rather than to abandon them altogether in favour of bounded rationality
Opinion Polarization by Learning from Social Feedback
We explore a new mechanism to explain polarization phenomena in opinion
dynamics in which agents evaluate alternative views on the basis of the social
feedback obtained on expressing them. High support of the favored opinion in
the social environment, is treated as a positive feedback which reinforces the
value associated to this opinion. In connected networks of sufficiently high
modularity, different groups of agents can form strong convictions of competing
opinions. Linking the social feedback process to standard equilibrium concepts
we analytically characterize sufficient conditions for the stability of
bi-polarization. While previous models have emphasized the polarization effects
of deliberative argument-based communication, our model highlights an affective
experience-based route to polarization, without assumptions about negative
influence or bounded confidence.Comment: Presented at the Social Simulation Conference (Dublin 2017
Mathematical models of games of chance: Epistemological taxonomy and potential in problem-gambling research
Games of chance are developed in their physical consumer-ready form on the basis of mathematical models, which stand as the premises of their existence and represent their physical processes. There is a prevalence of statistical and probabilistic models in the interest of all parties involved in the study of gambling â researchers, game producers and operators, and players â while functional models are of interest more to math-inclined players than problem-gambling researchers. In this paper I present a structural analysis of the knowledge attached to mathematical models of games of chance and the act of modeling, arguing that such knowledge holds potential in the prevention and cognitive treatment of excessive gambling, and I propose further research in this direction
Elaboration of the Model of Formation of Readiness of Future Primary School Teachers to the Use of Learning-playing Technologies
The study characterizes the structural-functional model of formation of readiness of students of the specialty âPrimary educationâ to using learning-playing technologies in the educational process. Among general modeling forms there was chosen the combined model (graphic descriptive scheme) of the structural-functional type. There was substantiated the main idea of modeling of the process of formation of future primary school teachers' readiness to using learning-playing technologies, especially, elaboration of such structural-functional model that would allow to improve the effectiveness of this process, to make it correspondent to social requirements and expectations from realization of New Ukrainian school principles. It was determined, that the object of modeling is the process of formation of readiness to using learning-playing technologies, realized within the general system of the professional training of future primary school teachers. The aim of the model creation was formulated: elaboration of the schematic construction that embodies the abstract structure and the real projected process and result. The theoretical-methodological approaches to the model projecting process were separated, namely: system, activity, personally oriented, competence. The main blocs of the elaborated structural-functional model were separated, especially, target, content, procedural and resulting
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
The Influence of Experimental and Computational Economics: Economics Back to the Future of Social Sciences
Economics has been a most puzzling science, namely since the neoclassical revolution defined the legitimate procedures for theorisation and quantification. Its epistemology has based on farce: decisive tests are not applied on dare predictions. As a consequence, estimation has finally been replaced by simulation, and empirical tests have been substituted by non-disciplined exercises of comparison of models with reality. Furthermore, the core concepts of economics defy the normally accepted semantics and tend to establish meanings of their own. One of the obvious instances is the notion of rationality, which has been generally equated with the apt use of formal logic or the ability to apply econometric estimation as a rule of thumb for daily life. In that sense, rationality is defined devoid of content, as alien to the construction of significance and reference by reason and social communication. The contradictory use of simulacra and automata, by John von Neumann and Herbert Simon, was a response to this escape of economic models from reality, suggesting that markets could be conceived of as complex institutions. But most mainstream economists did not understand or did not accept these novelties, and the empirical inquiry or the realistic representation of the action of agents and of their social interaction remained a minor domain of economics, and was essentially ignored by canonical theorizing. The argument of the current paper is based on a survey and discussion of the twin contributions of experimental and computational economics to these issues. Although mainly arising out of the mainstream, these emergent fields of economics generate challenging heuristics as well as new empirical results that defy orthodoxy. Their contributions both to the definition of the social meanings of rationality and to the definition of a new brand of inductive economics are discussed.
Emissions Trends and Labour Productivity Dynamics Sector Analyses of De-coupling/Recoupling on a 1990-2005 Namea
This paper provides new empirical evidence on Environmental Kuznets Curves (EKC) for greenhouse gases (GHGs) and air pollutants at sector level. A panel dataset based on the Italian NAMEA over 1990-2005 is analysed, focusing on both emission efficiency (EKC model) and total emissions (IPAT model). Results show that looking at sector evidence, both decoupling and also eventually re-coupling trends could emerge along the path of economic development. CH4 is moderately decreasing in recent years, but being a minor gas compared to CO2, the overall performance on GHGs is not compliant with Kyoto targets, which do not appear to have generated a structural break in the dynamics at least for GHGs. SOx and NOx show decreasing patterns, though the shape is affected by some outlier sectors with regard to joint emission-productivity dynamics, and for SOx exogenous innovation and policy related factors may be the main driving force behind observed reductions. Services tend to present stronger delinking patterns across emissions. Trade expansion validates the pollution haven in some cases, but also show negative signs when only EU15 trade is considered: this may due to technology spillovers and a positive ârace to the topâ rather than the bottom among EU15 trade partners (Italy and Germany as the main exporters and trade partner in the EU). Finally, general R&D expenditure show weak correlation with emissions efficiency. EKC and IPAT derived models provide similar conclusions overall; the emission-labour elasticity estimated in the latter is generally different from 1, suggesting that in most cases, and for both services and industry, a scenario characterised by emissions saving technological dynamics. Further research should be directed towards deeper investigation of trade relationship at sector level, increased research into and efforts to produce specific sectoral data on âenvironmental innovationsâ, and to verifying the value of heterogeneous panel models capturing sector heterogeneity.Greenhouse Gases, Air Pollutants, NAMEA, Trade Openness, Labour Productivity, EKC, STIRPAT, Delinking
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