A causal account of the brain network computations underlying strategic social behavior

During competitive interactions, humans have to estimate the impact of their own actions on their opponent's strategy. Here we provide evidence that neural computations in the right temporoparietal junction (rTPJ) and interconnected structures are causally involved in this process. By combining inhibitory continuous theta-burst transcranial magnetic stimulation with model-based functional MRI, we show that disrupting neural excitability in the rTPJ reduces behavioral and neural indices of mentalizing-related computations, as well as functional connectivity of the rTPJ with ventral and dorsal parts of the medial prefrontal cortex. These results provide a causal demonstration that neural computations instantiated in the rTPJ are neurobiological prerequisites for the ability to integrate opponent beliefs into strategic choice, through system-level interaction within the valuation and mentalizing networks

Modelling and Forecasting Daily Electricity Load via Curve Linear Regression

In this paper, we discuss the problem of short-term electricity load forecasting by regarding electricity load on each day as a curve. The dependence between successive daily loads and other relevant factors such as temperature, is modelled via curve linear regression where both the response and the regressor are functional (curves). The key ingredient of the proposed method is the dimension reduction based on the singular value decomposition in a Hilbert space, which reduces the curve linear regression problem to several ordinary (i.e. scalar) linear regression problems. This method has previously been adopted in the hybrid approach proposed by Cho et al. (J Am Stat Assoc 108: 7-21, 2013) for the same purpose, where the curve linear regression modelling was applied to the data after the trend and the seasonality were removed by a generalised additive model fitted at the weekly level. We show that classifying the successive daily loads prior to curve linear regression removes the necessity of such a two-stage approach as well as resulting in reducing the forecasting error by a great margin. The proposed methodology is illustrated using the electricity load dataset collected between 2007 and mid-2012, on which it is compared to the hybrid approach and other available competitors. Finally, various ways for improving the forecasting performance of the curve linear regression technique are discussed.EICPCI-S(ISTP)[email protected]

A unifying learning framework for building artificial game-playing agents

First online: 31 January 2015This paper investigates learning-based agents that are capable of mimicking human behavior in game playing, a central task in computational economics. Although computational economists have developed various game-playing agents, well-established machine learning methods such as graphical models have not been applied before. Leveraging probabilistic graphical models, this paper presents a novel sequential Bayesian network (SBN) framework for building artificial game-playing agents. We show that many existing agents, including reinforcement learning, fictitious play, and many of their variants, have a unified Bayesian explanation within the proposed SBN framework. Moreover, we discover that SBN can handle various important settings of game playing, allowing for a broad scope of its use in economics. SBN not only provides a unifying and satisfying framework to explain existing learning approaches in virtual economies, but also enables the development of new algorithms that are stronger or have fewer restrictions. In this paper, we derive a new algorithm, Hidden Markovian Play (HMP), from the generic SBN model to handle an important but difficult setting in which a player cannot observe the opponent’s strategy and payoff. It leverages Markovian learning to infer unobservable information, leading to higher quality of the agents. Experiments on real-world field experiments in evaluating economies show that our HMP model outperforms the baseline algorithms for building artificial agents