Farmland Control Decisions under Different Intertemporal Risk Behavioral Constructs

Simulation-optimization techniques are employed to analyze changes in farmland control arrangements as a result of using different constructs of intertemporal risk behavior. Risk behavior based on constant absolute risk aversion (CARA) and constant relative risk aversion (CRRA) mean-standard deviation functions are used to achieve this objective. Specfically, a multi-period programming framework for a representative grain farm is developed to explore farmland control decisions under these two behavioral assumptions. Our results suggest that the use of a CRRA behavioral construct in analyzing farmland control decisions produce predictions that are more consistent with observed farm behavior.Farm Management,

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables

Volatile Decision Dynamics: Experiments, Stochastic Description, Intermittency Control, and Traffic Optimization

The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc., they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions. We have also identified ways of information presentation that can considerably improve the overall performance of the system. In order to determine optimal strategies of decision guidance by means of user-specific recommendations, a stochastic behavioural description is developed. These strategies manage to increase the adaptibility to changing conditions and to reduce the deviation from the time-dependent user equilibrium, thereby enhancing the average and individual payoffs. Hence, our guidance strategies can increase the performance of all users by reducing overreaction and stabilizing the decision dynamics. These results are highly significant for predicting decision behaviour, for reaching optimal behavioural distributions by decision support systems, and for information service providers. One of the promising fields of application is traffic optimization.Comment: For related work see http://www.helbing.or

On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization

Revealed preference theory studies the possibility of modeling an agent's revealed preferences and the construction of a consistent utility function. However, modeling agent's choices over preference orderings is not always practical and demands strong assumptions on human rationality and data-acquisition abilities. Therefore, we propose a simple generative choice model where agents are assumed to generate the choice probabilities based on latent factor matrices that capture their choice evaluation across multiple attributes. Since the multi-attribute evaluation is typically hidden within the agent's psyche, we consider a signaling mechanism where agents are provided with choice information through private signals, so that the agent's choices provide more insight about his/her latent evaluation across multiple attributes. We estimate the choice model via a novel multi-stage matrix factorization algorithm that minimizes the average deviation of the factor estimates from choice data. Simulation results are presented to validate the estimation performance of our proposed algorithm.Comment: 6 pages, 2 figures, to be presented at CISS conferenc

International Adjudication and Custom Breaking by Domestic Courts

This Essay identifies a fundamental but overlooked tension between international adjudication and the evolution of customary international law (CIL). According to the traditional understanding, the evolution of CIL requires one or more states to deviate from existing customary rules and engage in new conduct—a concept that I refer to as custom breaking. A deviation\u27s legal status is determined over time, as other states respond by deciding whether to follow the proposed break or adhere to the existing rule. Therefore, the deviation cannot be classified definitively as either legal or illegal at the time it occurs. During the period of state response, CIL necessarily contains some legal ambiguity and inconsistency. Because an important function of international adjudication involves resolving ambiguities in the law, a central tension emerges: international courts may be called upon to adjudicate a break with CIL before other states have had the opportunity to decide for themselves whether to follow the break. Given that most international courts will invalidate deviations from the status quo, international adjudication risks impeding the traditional process by which CIL evolves. More specifically, international adjudication of cases that involve custom breaking may have both a procedural and a substantive effect: procedurally, it may short-circuit state responses to the break with CIL, and substantively, it may deter states from following the custom breaker, even when the other states are not formally bound by the international judicial decision. To illustrate these constraining effects, this Essay discusses three departures by domestic courts from the foreign sovereign immunity rule. It concludes by proposing that in cases involving custom breaking, international courts should adopt, as Professor Cass Sunstein has argued for U.S. courts, a minimalist approach that produces narrow and shallow decisions. This judicial strategy would give states, including their domestic courts, the opportunity to determine for themselves whether a break with international custom is the beginning of a new legal rule

How good are MatLab, Octave and Scilab for Computational Modelling?

In this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS). We submitted them to numerical tests using standard data sets and using the functions provided by each platform. A Monte Carlo study was conducted in some of the datasets in order to verify the stability of the results with respect to small departures from the original input. We propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known. We also used data provided by NIST (National Institute of Standards and Technology), a protocol which includes the computation of basic univariate statistics (mean, standard deviation and first-lag correlation), linear regression and extremes of probability distributions. The assessment was made comparing the results computed by the platforms with certified values, that is, known results, computing the number of correct significant digits.Comment: Accepted for publication in the Computational and Applied Mathematics journa

Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution