A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents’ previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model’s arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others

A Review of Energy Models. No. 3 (Special Issue on Soviet Models)

The experience of the USSR in the field of energy systems development modeling reveals certain patterns and principles that influence the structure and use of energy models, principally: -- The need to use mainly optimization models since, for planning purposes, optimal solutions must be found; -- The need to coordinate individual models in order to obtain the country's objectives; -- The existing organizational structure of planning which must be taken into account; -- The dependence of models on time aspects of planning (annual, 5-year, 15-year); -- The elaboration of corresponding methods for providing necessary input data. This has required the development of a special concept for optimizing energy systems development with the use of mathematical models. It is based on consideration of the energy industries of the country as complex with a hierarchical structure of energy systems of various territorial and branch levels. At the same time, the differentiation of aims at different times during the planning period have been taken into account. This concept is given here in its existing state (it is continuously developed and perfected) for better understanding of the energy models described. In particular, we show the role of the system of models for optimization of the energy supply system as a whole, and that of more detailed branch models (oil, gas, coal, electricity production systems). For optimal energy strategy evaluation, the most important models are those used on the highest levels of the energy systems hierarchy, i.e. the general (aggregate) energy systems of the country and of economic regions, and branch energy systems. Only these models are described here; models used on lower levels for solving some technical problems are far more diverse and numerous, and it is impossible to consider them all in a single review

A Review of Energy Models No.4 - July 1978

This review is the fourth in the IIASA series A REVIEW OF ENERGY MODELS (RR-74-10, No. 1 of May 1974 as revised in September 1976; RR-75-30, No. 2 of July 1975; and RR-77-13, No. 3, Special Issue of Soviet Models), which aims at wider diffusion of energy modeling work in progress at other institutions. Fourteen models are described in this issue and again classified in terms of substance and geographical applicability with further subdivision into groups corresponding to model user requirements: the majority of the models focus on the energy problem; they are mostly national ones involving either one or several kinds of fuel; six other models, both international and national, combine energy and overall economic aspects; they may be of particular interest for a more global consideration of energy problems

Simulation Application for the LHCb Experiment

We describe the LHCb detector simulation application (Gauss) based on the Geant4 toolkit. The application is built using the Gaudi software framework, which is used for all event-processing applications in the LHCb experiment. The existence of an underlying framework allows several common basic services such as persistency, interactivity, as well as detector geometry description or particle data to be shared between simulation, reconstruction and analysis applications. The main benefits of such common services are coherence between different event-processing stages as well as reduced development effort. The interfacing to Geant4 toolkit is realized through a facade (GiGa) which minimizes the coupling to the simulation engine and provides a set of abstract interfaces for configuration and event-by-event communication. The Gauss application is composed of three main blocks, i.e. event generation, detector response simulation and digitization which reflect the different stages performed during the simulation job. We describe the overall design as well as the details of Gauss application with a special emphasis on the configuration and control of the underlying simulation engine. We also briefly mention the validation strategy and the planing for the LHCb experiment simulation.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 6 pages, LaTeX, 9 eps figures. PSN TUMT00

A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning

When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents’ previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model’s arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others

Overview on Techniques and Models Used in the Energy Field

The so-called "energy crisis" has caused increasing concern in the energy industry and in governments. The procedures of environmental protection have further complicated the energy supply and price picture. The question of how serious an energy problem we are facing and of alternative strategies can be studied by means of extensive models. The adequacy of the available models and the possibilities of the different kinds of models under development are being exhaustively reviewed in most countries. The use of models for decision making in industry is quite well developed, while their use by governments, at national or international levels, is in the beginning stages. The paper begins with a brief review of the main techniques used in energy modeling to illustrate the extent of the term "model" which ranges from classical methods such as linear programming to the building of normative scenarios. A review of the most important models developed and being developed follows. Finally, some questions linked to a specific problem -- energy demand -- are posed