"Large Auction Design in Dominance"

This paper shows a detail-free idea of multi-object large double auction design in general trading environments, where the auctioneer randomly divides agents into two groups, and agents in each group trade at the market-clearing price vector in the other group. With private values, any dominant strategy profile mimics price-taking behavior, and the auctioneer achieves approximate efficiency. With interdependent values, any twice iteratively undominated strategy profile mimics fully revealing rational expectations equilibrium, and the auctioneer approximately achieves ex post efficiency. We need only a very weak common knowledge assumption on rationality.

Large Auction Design in Dominance

This paper shows a detail-free idea of multi-object large double auction design in general trading environments, where the auctioneer randomly divides agents into two groups, and agents in each group trade at the market-clearing price vector in the other group. With private values, any dominant strategy profile mimics price-taking behavior, and the auctioneer achieves approximate efficiency. With interdependent values, any twice iteratively undominated strategy profile mimics fully revealing rational expectations equilibrium, and the auctioneer approximately achieves ex post efficiency. We need only a very weak common knowledge assumption on rationality.

Sequential Decision Making with Untrustworthy Service Providers

In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of service providers populating the environment. Specifically, we propose a generic Bayesian trust model, and formulate the optimal Bayesian solution to the exploration-exploitation problem facing the agents when repeatedly interacting with others in such environments. We then present a computationally tractable Bayesian reinforcement learning algorithm to approximate that solution by taking into account the expected value of perfect information of an agent's actions. Our algorithm is shown to dramatically outperform all previous finalists of the international Agent Reputation and Trust (ART) competition, including the winner from both years the competition has been run

Selfish Knapsack

We consider a selfish variant of the knapsack problem. In our version, the items are owned by agents, and each agent can misrepresent the set of items she owns---either by avoiding reporting some of them (understating), or by reporting additional ones that do not exist (overstating). Each agent's objective is to maximize, within the items chosen for inclusion in the knapsack, the total valuation of her own chosen items. The knapsack problem, in this context, seeks to minimize the worst-case approximation ratio for social welfare at equilibrium. We show that a randomized greedy mechanism has attractive strategic properties: in general, it has a correlated price of anarchy of $2$ (subject to a mild assumption). For overstating-only agents, it becomes strategyproof; we also provide a matching lower bound of $2$ on the (worst-case) approximation ratio attainable by randomized strategyproof mechanisms, and show that no deterministic strategyproof mechanism can provide any constant approximation ratio. We also deal with more specialized environments. For the case of $2$ understating-only agents, we provide a randomized strategyproof $\frac{5+4\sqrt{2}}{7} \approx 1.522$-approximate mechanism, and a lower bound of $\frac{5\sqrt{5}-9}{2} \approx 1.09$. When all agents but one are honest, we provide a deterministic strategyproof $\frac{1+\sqrt{5}}{2} \approx 1.618$-approximate mechanism with a matching lower bound. Finally, we consider a model where agents can misreport their items' properties rather than existence. Specifically, each agent owns a single item, whose value-to-size ratio is publicly known, but whose actual value and size are not. We show that an adaptation of the greedy mechanism is strategyproof and $2$-approximate, and provide a matching lower bound; we also show that no deterministic strategyproof mechanism can provide a constant approximation ratio

Approximate Equilibrium Asset Prices.

Arguing that total consumer wealth is unobservable, we invert the (approximate) consumption function to reconstruct, in a world with Kreps-Porteus generalized isoelastic preferences, i) the wealth that supports the agents’ observed consumption as an optimal outcome and ii) the rate of return on the consumers’ wealth portfolio. This allows us to (approximately) price assets solely as a function of their payoffs and of consumption — in both homoskedastic or heteroskedastic environments. We compare implied equilibrium returns on the wealth portfolio to observed stock market returns and gauge whether the stock market is a good proxy for unobserved aggregate wealth.Asset pricing, Kreps-Porteus, Epstein-Zin-Weil preferences;

Reasoning about norms under uncertainty in dynamic environments

The behaviour of norm-autonomous agents is determined by their goals and the norms that are explicitly represented inside their minds. Thus, they require mechanisms for acquiring and accepting norms, determining when norms are relevant to their case, and making decisions about norm compliance. Up un- til now the existing proposals on norm-autonomous agents assume that agents interact within a deterministic environment that is certainly perceived. In prac- tice, agents interact by means of sensors and actuators under uncertainty with non-deterministic and dynamic environments. Therefore, the existing propos- als are unsuitable or, even, useless to be applied when agents have a physical presence in some real-world environment. In response to this problem we have developed the n-BDI architecture. In this paper, we propose a multi -context graded BDI architecture (called n-BDI) that models norm-autonomous agents able to deal with uncertainty in dynamic environments. The n-BDI architecture has been experimentally evaluated and the results are shown in this paper.This paper was partially funded by the Spanish government under Grant CONSOLIDER-INGENIO 2010 CSD2007-00022 and the Valencian government under Project PROMETEOH/2013/019.Criado Pacheco, N.; Argente, E.; Noriega, P.; Botti Navarro, VJ. (2014). Reasoning about norms under uncertainty in dynamic environments. International Journal of Approximate Reasoning. 55(9):2049-2070. https://doi.org/10.1016/j.ijar.2014.02.004S2049207055

Multiplicity of mixed equilibria in mechanisms: A unified approach to exact and approximate implementation

We characterize full implementation of social choice sets in mixed strategy Bayesian equilibrium. Our results concern both exact and virtual mixed implementation. For exact implementation, we identify a strengthening of Bayesian monotonicity, which we refer to as mixed Bayesian monotonicity. It is shown that, in economic environments with at least three agents, mixed Bayesian implementation is equivalent to mixed Bayesian monotonicity, incentive compatibility and closure. For implementing a social choice function, the case of two-agents is also covered by these conditions and mixed Bayesian monotonicity reduces to Bayesian monotonicity. Following parallel steps, mixed virtual implementation is shown to be equivalent to mixed virtual monotonicity, incentive compatibility and closure. The key condition, mixed virtual monotonicity, is argued to be very weak. In particular, it is weaker than Abreu-Matsushima's measurability, there by implying that: (1) virtual implementation in mixed Bayesian equilibrium is more permissive than virtual implementation in iteratively undominated strategies, and (2) non-regular mechanisms are essential for the implementation of rules in that gap.exact implementation; approximate implementation; incomplete information; incentive compatibility; monotonicity

Learning Causal State Representations of Partially Observable Environments

Intelligent agents can cope with sensory-rich environments by learning task-agnostic state abstractions. In this paper, we propose mechanisms to approximate causal states, which optimally compress the joint history of actions and observations in partially-observable Markov decision processes. Our proposed algorithm extracts causal state representations from RNNs that are trained to predict subsequent observations given the history. We demonstrate that these learned task-agnostic state abstractions can be used to efficiently learn policies for reinforcement learning problems with rich observation spaces. We evaluate agents using multiple partially observable navigation tasks with both discrete (GridWorld) and continuous (VizDoom, ALE) observation processes that cannot be solved by traditional memory-limited methods. Our experiments demonstrate systematic improvement of the DQN and tabular models using approximate causal state representations with respect to recurrent-DQN baselines trained with raw inputs