Dataset Information

Policy adjustment in a dynamic economic game.

ABSTRACT: Making sequential decisions to harvest rewards is a notoriously difficult problem. One difficulty is that the real world is not stationary and the reward expected from a contemplated action may depend in complex ways on the history of an animal's choices. Previous functional neuroimaging work combined with principled models has detected brain responses that correlate with computations thought to guide simple learning and action choice. Those works generally employed instrumental conditioning tasks with fixed action-reward contingencies. For real-world learning problems, the history of reward-harvesting choices can change the likelihood of rewards collected by the same choices in the near-term future. We used functional MRI to probe brain and behavioral responses in a continuous decision-making task where reward contingency is a function of both a subject's immediate choice and his choice history. In these more complex tasks, we demonstrated that a simple actor-critic model can account for both the subjects' behavioral and brain responses, and identified a reward prediction error signal in ventral striatal structures active during these non-stationary decision tasks. However, a sudden introduction of new reward structures engages more complex control circuitry in the prefrontal cortex (inferior frontal gyrus and anterior insula) and is not captured by a simple actor-critic model. Taken together, these results extend our knowledge of reward-learning signals into more complex, history-dependent choice tasks. They also highlight the important interplay between striatum and prefrontal cortex as decision-makers respond to the strategic demands imposed by non-stationary reward environments more reminiscent of real-world tasks.

SUBMITTER: Li J

PROVIDER: S-EPMC1762366 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature

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Policy adjustment in a dynamic economic game.

Li Jian J McClure Samuel M SM King-Casas Brooks B Montague P Read PR

PloS one 20061220

Making sequential decisions to harvest rewards is a notoriously difficult problem. One difficulty is that the real world is not stationary and the reward expected from a contemplated action may depend in complex ways on the history of an animal's choices. Previous functional neuroimaging work combined with principled models has detected brain responses that correlate with computations thought to guide simple learning and action choice. Those works generally employed instrumental conditioning tas ...[more]

PMID: 17183636

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Project description:Background Mental illness costs the world economy over US2.5 Bn each year, including premature mortality, morbidity, and productivity losses. Multisector approaches are required to address the systemic drivers of mental health and ensure adequate service provision. There is an important role for economics to support priority setting, identify best value investments and inform optimal implementation. Mental health can be defined as a complex dynamic system where decision makers are challenged to prospectively manage the system over time. This protocol describes the approach to equip eight system dynamics (SD) models across Australia to support priority setting and guide portfolio investment decisions, tailored to local implementation context. Methods As part of a multidisciplinary team, three interlinked protocols are developed; (i) the participatory process to codesign the models with local stakeholders and identify interventions for implementation, (ii) the technical protocol to develop the SD models to simulate the dynamics of the local population, drivers of mental health, the service system and clinical outcomes, and (iii) the economic protocol to detail how the SD models will be equipped to undertake a suite of economic analysis, incorporating health and societal perspectives. Models will estimate the cost of mental illness, inclusive of service costs (health and other sectors, where necessary), quality-adjusted life years (QALYs) lost, productivity costs and carer costs. To assess the value of investing (disinvesting) in interventions, economic analysis will include return-on-investment, cost-utility, cost benefit, and budget impact to inform affordability. Economic metrics are expected to be dynamic, conditional upon changing population demographics, service system capacities and the mix of interventions when synergetic or antagonistic interactions. To support priority setting, a portfolio approach will identify best value combinations of interventions, relative to a defined budget(s). User friendly dashboards will guide decision makers to use the SD models to inform resource allocation and generate business cases for funding. Discussion Equipping SD models to undertake economic analysis is intended to support local priority setting and help optimise implementation regarding the best value mix of investments, timing and scale. The objectives are to improve allocative efficiency, increase mental health and economic productivity.

Dataset Information

Policy adjustment in a dynamic economic game.

Publications

Policy adjustment in a dynamic economic game.

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