Artificial intelligence (AI) and reinforcement learning (RL) have improved many areas but are not yet widely adopted in economic policy design, mechanism design, or economics at large. The AI Economist is a two-level, deep RL framework for policy design in which agents and a social planner coadapt. In particular, the AI Economist uses structured curriculum learning to stabilize the challenging two-level, coadaptive learning problem. We validate this framework in the domain of taxation. In one-step economies, the AI Economist recovers the optimal tax policy of economic theory. In spatiotemporal economies, the AI Economist substantially improves both utilitarian social welfare and the trade-off between equality and productivity over baselines. It does so despite emergent tax-gaming strategies while accounting for emergent labor specialization, agent interactions, and behavioral change. These results demonstrate that two-level, deep RL complements economic theory and unlocks an AI-based approach to designing and understanding economic policy.

In primary visual cortex (V1), neuronal responses are sensitive to context. For example, responses to stimuli presented within the receptive field (RF) center are often suppressed by stimuli within the RF surround, and this suppression tends to be strongest when the center and surround stimuli match. We sought to identify the mechanism that gives rise to these properties of surround modulation. To do so, we exploited the stability of implanted multielectrode arrays to record from neurons in V1 of alert monkeys with multiple stimulus sets that more exhaustively probed center-surround interactions. We first replicated previous results concerning center-surround similarity using gratings representing all combinations of center and surround orientation. With this stimulus set, the surround simply scaled population responses to the center, such that the overall population tuning curve had the same shape and peak response. However, when the center contained two superimposed gratings (i.e., a visual "plaid"), one component of which always matched the surround orientation, suppression selectively affected the portion of the response driven by the matching center component, thereby producing shifts in the peak of the population orientation tuning curve. In effect, the surround caused neurons to respond predominantly to the component grating of the center plaid that was unmatched to the surround grating, as if by reducing the effective strength of whichever stimulus attributes were matched to the surround. These results provide key physiological support for theoretical models that propose feature-specific, input-gain control as the mechanism underlying surround suppression.

Mate selection is critical to ensuring the survival of a species. In the fruit fly, Drosophila melanogaster, genetic and anatomical studies have focused on mate recognition and courtship initiation for decades. This model system has proven to be highly amenable for the study of neural systems controlling the decision making process. However, much less is known about how courtship quality is regulated in a temporally dynamic manner in males and how a female assesses male performance as she makes her decision of whether to accept copulation. Here, we report that the courting male dynamically adjusts the relative proportions of the song components, pulse song or sine song, by assessing female locomotion. Male flies deficient for olfaction failed to perform the locomotion-dependent song modulation, indicating that olfactory cues provide essential information regarding proximity to the target female. Olfactory mutant males also showed lower copulation success when paired with wild-type females, suggesting that the male’s ability to temporally control song significantly affects female mating receptivity. These results depict the consecutive inter-sex behavioral decisions, in which a male smells the close proximity of a female as an indication of her increased receptivity and accordingly coordinates his song choice, which then enhances the probability of his successful copulation.

Most approaches to visual prostheses have focused on the retina, and for good reasons. The earlier that one introduces signals into the visual system, the more one can take advantage of its prodigious computational abilities. For methods that make use of microelectrodes to introduce electrical signals, however, the limited density and volume occupying nature of the electrodes place severe limits on the image resolution that can be provided to the brain. In this regard, non-retinal areas in general, and the primary visual cortex in particular, possess one large advantage: “magnification factor” (MF)—a value that represents the distance across a sheet of neurons that represents a given angle of the visual field. In the foveal representation of primate primary visual cortex, the MF is enormous—on the order of 15–20 mm/deg in monkeys and humans, whereas on the retina, the MF is limited by the optical design of the eye to around 0.3 mm/deg. This means that, for an electrode array of a given density, a much higher- resolution image can be introduced into V1 than onto the retina (or any other visual structure). In addition to this tremendous advantage in resolution, visual cortex is plastic at many different levels ranging from a very local ability to learn to better detect electrical stimulation to higher levels of learning that permit human observers to adapt to radical changes to their visual inputs. We argue that the combination of the large magnification factor and the impressive ability of the cerebral cortex to learn to recognize arbitrary patterns, might outweigh the disadvantages of bypassing earlier processing stages and makes V1 a viable option for the restoration of vision.

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