El Aporte de la Psicología Experimental y las Neurociencias a las Políticas Públicas

Resumen El interés por aplicar teorías psicológicas y  neurocientíficas a la ciencia económica ha aumentado notablemente en las últimas décadas. Evidencia convergente desde distintas áreas sugiere la existencia de múltiples sistemas decisionales, comprendiendo al menos uno racional y deliberativo, y otro automático e irreflexivo. Recientemente, diversos gobiernos han intentado usar esta evidencia para modificar las decisiones tomadas por los ciudadanos de forma de aumentar el bienestar de la población a la vez que reducir el gasto público. Aunque las ideas provenientes de la Economía Conductual asemejan a las teorías psicológicas de modificación de decisiones mediante recompensas y castigos proveniente del conductismo Skinneriano, los sistemas decisionales afectados por tales Políticas Públicas no interfieren sobre la libertad de los individuos y son, en este sentido, neutrales políticamente. Palabras clave: Economía conductual, sesgos, políticas públicas, psicología experimental, toma de decisiones, teoría de la prospección, irracionalidad Abstract The contribution of Psychology and Neuroscience to Public Policy There has been an increasing Interest in recent years to apply ideas from psychology and neuroscience into economics. Converging evidence from different areas of research suggests the existence of multiple decisional systems controlling behaviour - one rational and deliberative, and another automatic and involuntary. A number of governments have started using this evidence to modify people’s decisions with the goal of increasing social well-being and reduce public spending. Although these ideas can be traced back to the behaviourist tradition of reward and punishment, the decisional systems that these policies aim to affect do not interfere with freedom of choice and are, in that sense, politically neutral. Keywords: Behavioural Economics, Cognitive Biases, Public Policy, Experimental Psychology, Decision-making, Prospect Theory, Irrationalit

High-resolution prestack seismic inversion using a hybrid FISTA least-squares strategy

A new inversion method to estimate high-resolution amplitude-versus-angle attributes (AVA) attributes such as intercept and gradient from prestack data is presented. The proposed technique promotes sparse-spike reflectivities that, when convolved with the source wavelet, fit the observed data. The inversion is carried out using a hybrid two-step strategy that combines fast iterative shrinkagethresholding algorithm (FISTA) and a standard least-squares (LS) inversion. FISTA, which can be viewed as an extension of the classical gradient algorithm, provides sparse solutions by minimizing the misfit between the modeled and the observed data, and the l1-norm of the solution. FISTA is used to estimate the location in time of the main reflectors. Then, LS is used to retrieve the appropriate reflectivity amplitudes that honor the data. FISTA, like other iterative solvers for l1-norm regularization, does not require matrices in explicit form, making it easy to apply, economic in computational terms, and adequate for solving large-scale problems. As a consequence, the FISTA+LS strategy represents a simple and cost-effective new procedure to solve the AVA inversion problem. Results on synthetic and field data show that the proposed hybrid method can obtain highresolution AVA attributes from noisy observations, making it an interesting alternative to conventional methods.Facultad de Ciencias Astronómicas y Geofísica

A hybrid strategy based on fast iterative shrinkage-thresholding algorithm and very fast simulated annealing: application to the prestack seismic inverse problem

With the purpose of characterizing the Earth subsurface, one of the objectives of the inversion of prestack seismic data is to determine contrasts between rock properties from the information contained in the variation of the amplitudes of the reflected compressional waves with the angle of incidence. This amplitude-versus-angle (AVA) variation can be described by various approximations to the so-called Zoeppritz equations, a set of non-linear equations that depend on the physical characteristics of the medium at each side of the interface where the compressional wave strikes. The coefficients of such approximations constitute AVA attributes that may provide important information about fluid content, a key issue for the characterization of hydrocarbon reservoirs. In this work we present a new inversion strategy to estimate efficiently and accurately high-resolution AVA attributes from prestack data. The proposed technique promotes sparse-spike reflectivities that, when convolved with the source wavelet, fit the observed data. Sparse solutions are desirable because they can be used to characterize significant and close reflectors more accurately than using conventional solutions. The inversion is carried out using a hybrid two-step strategy than combines Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Very Fast Simulated Annealing (VFSA). FISTA provides sparse solutions by minimizing both the misfit between the modeled and the observed data, and the l1-norm of the solution. VFSA is an stochastic computational algorithm to finding near-optimal solutions to hard optimization problems. At the first stage, FISTA sparse-solutions provide an estimate of the location in time of the main reflectors, information that is subsequently used as an initial guess for the second stage, where accurate reflectivity amplitudes are estimated by solving a more stable overdetermined inverse problem. The second stage also involves the use of VFSA for tuning the location in time of the main reflectors and the source wavelet. FISTA does not require the inversion of matrices in explicit form. At each iteration only matrix-vector multiplications are involved, making it easy to apply, economic in computational terms, and adequate for solving large-scale problems. As a result, the FISTA+VFSA strategy represents a simple and cost-effective new procedure to solve the high-resolution AVA inversion problem. Results on synthetic data show that the proposed hybrid method can obtain high-resolution AVA attributes from noisy observations, even when the number of reflectors is not known a priori and the utilized wavelet is inaccurate, making it an interesting alternative to conventional methods.Facultad de Ciencias Astronómicas y Geofísica

A hybrid strategy based on fast iterative shrinkage-thresholding algorithm and very fast simulated annealing: application to the prestack seismic inverse problem

With the purpose of characterizing the Earth subsurface, one of the objectives of the inversion of prestack seismic data is to determine contrasts between rock properties from the information contained in the variation of the amplitudes of the reflected compressional waves with the angle of incidence. This amplitude-versus-angle (AVA) variation can be described by various approximations to the so-called Zoeppritz equations, a set of non-linear equations that depend on the physical characteristics of the medium at each side of the interface where the compressional wave strikes. The coefficients of such approximations constitute AVA attributes that may provide important information about fluid content, a key issue for the characterization of hydrocarbon reservoirs. In this work we present a new inversion strategy to estimate efficiently and accurately high-resolution AVA attributes from prestack data. The proposed technique promotes sparse-spike reflectivities that, when convolved with the source wavelet, fit the observed data. Sparse solutions are desirable because they can be used to characterize significant and close reflectors more accurately than using conventional solutions. The inversion is carried out using a hybrid two-step strategy than combines Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Very Fast Simulated Annealing (VFSA). FISTA provides sparse solutions by minimizing both the misfit between the modeled and the observed data, and the l1-norm of the solution. VFSA is an stochastic computational algorithm to finding near-optimal solutions to hard optimization problems. At the first stage, FISTA sparse-solutions provide an estimate of the location in time of the main reflectors, information that is subsequently used as an initial guess for the second stage, where accurate reflectivity amplitudes are estimated by solving a more stable overdetermined inverse problem. The second stage also involves the use of VFSA for tuning the location in time of the main reflectors and the source wavelet. FISTA does not require the inversion of matrices in explicit form. At each iteration only matrix-vector multiplications are involved, making it easy to apply, economic in computational terms, and adequate for solving large-scale problems. As a result, the FISTA+VFSA strategy represents a simple and cost-effective new procedure to solve the high-resolution AVA inversion problem. Results on synthetic data show that the proposed hybrid method can obtain high-resolution AVA attributes from noisy observations, even when the number of reflectors is not known a priori and the utilized wavelet is inaccurate, making it an interesting alternative to conventional methods.Facultad de Ciencias Astronómicas y Geofísica

Ambiguity Drives Higher-Order Pavlovian Learning

In the natural world, stimulus-outcome associations are often noisy and ambiguous. Learning to disambiguate these associations to identify which specific outcomes will occur is critical for survival. Pavlovian occasion setters are stimuli that determine whether other stimuli that are ambiguous will result in a specific outcome. Occasion setting is a well-established field, but very little investigation has been conducted on how occasion setters are disambiguated when they themselves are ambiguous. We investigated the role of higher-order Pavlovian occasion setting in humans. We also developed and tested the first computational model predicting direct associations, traditional occasion setting, and 2nd-order occasion setting. Results showed that occasion setters affected ambiguous but not unambiguous lower-order stimuli and that 2nd-order occasion setting was indeed learned. Our computational model demonstrated excellent fit with the data, advancing our theoretical understanding of learning with ambiguity. These results may ultimately improve treatment of Pavlovian-based mental health disorders (e.g., anxiety)

A theory of actions and habits: The interaction of rate correlation and contiguity systems in free-operant behavior

Contemporary theories of instrumental performance assume that responding can be controlled by 2 behavioral systems, 1 goal-directed that encodes the outcome of an action, and 1 habitual that reinforces the response strength of the same action. Here we present a model of free-operant behavior in which goal-directed control is determined by the correlation between the rates of the action and the outcome whereas the total prediction error generated by contiguous reinforcement by the outcome controls habitual response strength. The outputs of these two systems summate to generate a total response strength. This cooperative model addresses the difference in the behavioral impact of ratio and interval schedules, the transition from goal-directed to habitual control with extended training, the persistence of goal-directed control under choice procedures and following extinction, among other phenomena. In these respects, this dual-system model is unique in its account of free-operant behavior