Dynamics of the Presidential Veto: A Computational

We specify and compute equilibria of a dynamic policy-making game between a president and a legislature under insitutional rules that emulate those of the US Constitution. Policies are assumed to lie in a two-dimensional space in which one issue dimension captures systemic differences in partisan preferences, while the other summarizes non-partisan attributes of policy. In any period, the policy choices of politicians are influenced by the position of the status quo policy in this space, with the current policy outcome determining the location of the status quo in the next period. Partisan control of the legislature and presidency changes probabilistically over time. We find that politicians strategically compromise their ideal policy in equilibrium, and that the degree of compromise increases when the opposition party is more likely to take control of the legislature in the next period, while politicians become relatively more extreme when the opposition party is more likely to control the presidency. We measure gridlock by (the inverse of ) the expected distance of enacted policies from the status quo in the long run, and we show that both gridlock and the long run welfare of a representative voter are maximized when government is divided without a super majority in the legislature. Under unified government, we find that the endogeneity of the status quo leads to a non-monotonic effect of the size of the legislative ma jority on gridlock; surprisingly, under unified government, gridlock is higher when the party in control of the legislature has a superma jority than when it has a bare ma jority. Furthermore, a relatively larger component of policy change occurs in the non-partisan policy dimension when a superma jority controls the legislature. We conduct constitutional experiments, and we find that voter welfare is minimized when the veto override provision is abolished and maximized when the presidential veto is abolished.

Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip

Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, non-fault tolerant, quantum devices. Here we report experimental results demonstrating that this intuition need not be true. We implement a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a Silicon quantum photonic device. The approach is verified to be well suited for pre-threshold quantum processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. This shows a promising route to unlock the power of quantum phase estimation much sooner than previously believed

Inverse Reinforcement Learning in Swarm Systems

Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming behavior of natural systems. In particular, we make the following contributions to the field: 1) We introduce the swarMDP framework, a sub-class of decentralized partially observable Markov decision processes endowed with a swarm characterization. 2) Exploiting the inherent homogeneity of this framework, we reduce the resulting multi-agent IRL problem to a single-agent one by proving that the agent-specific value functions in this model coincide. 3) To solve the corresponding control problem, we propose a novel heterogeneous learning scheme that is particularly tailored to the swarm setting. Results on two example systems demonstrate that our framework is able to produce meaningful local reward models from which we can replicate the observed global system dynamics.Comment: 9 pages, 8 figures; ### Version 2 ### version accepted at AAMAS 201