Indirect tax reform and the role of exemptions

This paper examines the question of whether indirect tax rates should be uniform, using four different modelling strategies. First, marginal tax reform is examined. This is concerned with the optimal direction of small changes in effective indirect tax rates and requires considerably less information than the calculation of optimal rates. Second, the welfare effects of a partial shift from the current indirect tax system in Australia towards a goods and services tax (GST) are considered, with particular emphasis on differences between household types and the role of exemptions. Third, in view of the stress on a distributional role for exemptions of certain goods from a GST, the potential limits to such redistribution are considered. The fourth approach examines the extent of horizontal inequity and reranking that can arise when there are non-uniform tax rates. These inequities arise essentially because of preference heterogeneity.

Comparing approaches for model-checking strategies under imperfect information and fairness constraints

Starting from Alternating-time Temporal Logic, many logics for reasoning about strategies in a system of agents have been proposed. Some of them consider the strategies that agents can play when they have partial information about the state of the system. ATLKirF is such a logic to reason about uniform strategies under unconditional fairness constraints. While this kind of logics has been extensively studied, practical approaches for solving their model- checking problem appeared only recently. This paper considers three approaches for model checking strategies under partial observability of the agents, applied to ATLKirF . These three approaches have been implemented in PyNuSMV, a Python library based on the state-of- the-art model checker NuSMV. Thanks to the experimental results obtained with this library and thanks to the comparison of the relative performance of the approaches, this paper provides indications and guidelines for the use of these verification techniques, showing that different approaches are needed in different situations

Strategic Abilities of Forgetful Agents in Stochastic Environments

In this paper, we investigate the probabilistic variants of the strategy logics ATL and ATL* under imperfect information. Specifically, we present novel decidability and complexity results when the model transitions are stochastic and agents play uniform strategies. That is, the semantics of the logics are based on multi-agent, stochastic transition systems with imperfect information, which combine two sources of uncertainty, namely, the partial observability agents have on the environment, and the likelihood of transitions to occur from a system state. Since the model checking problem is undecidable in general in this setting, we restrict our attention to agents with memoryless (positional) strategies. The resulting setting captures the situation in which agents have qualitative uncertainty of the local state and quantitative uncertainty about the occurrence of future events. We illustrate the usefulness of this setting with meaningful examples

PRECISION AGRICULTURE: ECONOMICS OF NITROGEN MANAGEMENT IN CORN USING SITE-SPECIFIC CROP RESPONSE ESTIMATES FROM A SPATIAL REGRESSION MODEL

Adapting variable rate technology (VRT) to Argentine conditions requires methods that use inexpensive information and that focus on the inputs and variability common to Argentine maize and soybean growing areas. The goal of this study is to determine if spatial regression analysis of yield monitor data can be used to estimate the site-specific crop Nitrogen (N) response needed to fine tune variable rate fertilizer strategies. N has been chosen as the focus of this study because it is the most commonly used fertilizer by corn farmers in Argentina. The methodology uses yield monitor data from on-farm trials to estimate site-specific crop response functions. The design involves a strip trial with a uniform N rate along the strip and a randomized complete block design, with regression estimation of N response curves by landscape position. Spatial autocorrelation and spatial heterogeneity are taken into account using a spatial error model and a groupwise heteroskedasticity model. A partial budget is used to calculate uniform rate and VRT returns. First year data indicate that N response differs significantly by landscape position, and that VRA for N may be modestly profitable on some locations depending on the VRT fee level, compared to a uniform rate of urea of 80kg ha-1. A more complete analysis will pool data over many farms and several years to determine if reliable differences exist in N response by landscape position or other type of management zone. The study is planned for four years. The purpose of this preliminary analysis is to show how spatial regression analysis of yield data could be used to fine tune input use.Crop Production/Industries,

A Backward-traversal-based Approach for Symbolic Model Checking of Uniform Strategies for Constrained Reachability

Since the introduction of Alternating-time Temporal Logic (ATL), many logics have been proposed to reason about different strategic capabilities of the agents of a system. In particular, some logics have been designed to reason about the uniform memoryless strategies of such agents. These strategies are the ones the agents can effectively play by only looking at what they observe from the current state. ATL_ir can be seen as the core logic to reason about such uniform strategies. Nevertheless, its model-checking problem is difficult (it requires a polynomial number of calls to an NP oracle), and practical algorithms to solve it appeared only recently. This paper proposes a technique for model checking uniform memoryless strategies. Existing techniques build the strategies from the states of interest, such as the initial states, through a forward traversal of the system. On the other hand, the proposed approach builds the winning strategies from the target states through a backward traversal, making sure that only uniform strategies are explored. Nevertheless, building the strategies from the ground up limits its applicability to constrained reachability objectives only. This paper describes the approach in details and compares it experimentally with existing approaches implemented into a BDD-based framework. These experiments show that the technique is competitive on the cases it can handle.Comment: In Proceedings GandALF 2017, arXiv:1709.0176

ATLsc with partial observation

Alternating-time temporal logic with strategy contexts (ATLsc) is a powerful formalism for expressing properties of multi-agent systems: it extends CTL with strategy quantifiers, offering a convenient way of expressing both collaboration and antagonism between several agents. Incomplete observation of the state space is a desirable feature in such a framework, but it quickly leads to undecidable verification problems. In this paper, we prove that uniform incomplete observation (where all players have the same observation) preserves decidability of the model-checking problem, even for very expressive logics such as ATLsc.Comment: In Proceedings GandALF 2015, arXiv:1509.0685

Randomness for Free

We consider two-player zero-sum games on graphs. These games can be classified on the basis of the information of the players and on the mode of interaction between them. On the basis of information the classification is as follows: (a) partial-observation (both players have partial view of the game); (b) one-sided complete-observation (one player has complete observation); and (c) complete-observation (both players have complete view of the game). On the basis of mode of interaction we have the following classification: (a) concurrent (both players interact simultaneously); and (b) turn-based (both players interact in turn). The two sources of randomness in these games are randomness in transition function and randomness in strategies. In general, randomized strategies are more powerful than deterministic strategies, and randomness in transitions gives more general classes of games. In this work we present a complete characterization for the classes of games where randomness is not helpful in: (a) the transition function probabilistic transition can be simulated by deterministic transition); and (b) strategies (pure strategies are as powerful as randomized strategies). As consequence of our characterization we obtain new undecidability results for these games

Thresholding at the monopoly price: an agnostic way to improve bidding strategies in revenue-maximizing auctions

We address the problem of improving bidders' strategies in prior-dependent revenue-maximizing auctions. We introduce a simple and generic method to design novel bidding strategies if the seller uses past bids to optimize her mechanism. This strategy works with general value distributions, with asymmetric bidders and for different revenue-maximizing mechanisms. Furthermore, it can be made robust to sample approximation errors on the seller part. This results in a large increase in utility for bidders whether they have a full or partial knowledge of their competitors. In the case where the buyer has no information about the competition, we propose a simple and agnostic strategy that is robust to mechanism changes and local (as opposed to global) optimization of e.g. reserve prices by the seller. In textbook-style examples, for instance with uniform value distributions and two bidders, this no-side-information and mechanism-independent strategy yields an enormous 57% increase in buyer utility for lazy second price auctions with no reserves. In the i.i.d symmetric case, we show existence and uniqueness of a Nash equilibrium in the class of strategy we consider for lazy second price auctions, as well as the corresponding explicit shading strategies. Our approach also works for Myerson auctions for instance. At this Nash equilibrium, buyer's utility is the same as in a second price auction with no reserve. Our approach also yields optimal solutions when buyer are constrained in the class of shading strategies they can use, a realistic constraint in practical applications. The heart of our approach is to see optimal auctions in practice as a Stackelberg game where the buyer is the leader, as he is the first one to move (here bid) when the seller is the follower as she has no prior information on the bidder

Strategy Logic with Imperfect Information

We introduce an extension of Strategy Logic for the imperfect-information setting, called SLii, and study its model-checking problem. As this logic naturally captures multi-player games with imperfect information, the problem turns out to be undecidable. We introduce a syntactical class of "hierarchical instances" for which, intuitively, as one goes down the syntactic tree of the formula, strategy quantifications are concerned with finer observations of the model. We prove that model-checking SLii restricted to hierarchical instances is decidable. This result, because it allows for complex patterns of existential and universal quantification on strategies, greatly generalises previous ones, such as decidability of multi-player games with imperfect information and hierarchical observations, and decidability of distributed synthesis for hierarchical systems. To establish the decidability result, we introduce and study QCTL*ii, an extension of QCTL* (itself an extension of CTL* with second-order quantification over atomic propositions) by parameterising its quantifiers with observations. The simple syntax of QCTL* ii allows us to provide a conceptually neat reduction of SLii to QCTL*ii that separates concerns, allowing one to forget about strategies and players and focus solely on second-order quantification. While the model-checking problem of QCTL*ii is, in general, undecidable, we identify a syntactic fragment of hierarchical formulas and prove, using an automata-theoretic approach, that it is decidable. The decidability result for SLii follows since the reduction maps hierarchical instances of SLii to hierarchical formulas of QCTL*ii