Randomization in individual choice behavior.

There is ample evidence that people cannot generate random series when instructed to do so. Rather, they produce sequences with too few symmetries and long runs and too many alternations among events. The authors propose a psychological theory to account for these findings, which assumes that subjects generate nonrandom sequences that locally represent theoretical random series subject to a constraint on their short-term memory. Closed-form expressions are then derived for the major statistics that have been used to test for deviations from randomness. Results from 3 experiments with 2 and 3 equiprobable alternatives support the model on both the individual and group levels. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Provision of step-level public goods: Effects of inequality in resources.

Twelve groups of 5 players each participated in a multiple-trial social dilemma game in which each player, i, receives a possibly different monetary endowment, ei, and then chooses independently and anonymously whether to contribute it to a monetary public good. The collective good, r, is supplied to all of the group members if the sum of contributions is equal to or larger than a prespecified and known threshold; it is not supplied otherwise. It was found (a) that the proportion of contributions decreased with experience, (b) that players contributed more often, the higher their endowment, but (c) that players with lower endowments were perceived to be more likely to contribute than were players with higher endowments. Group data provided strong support to a model in which the decision to contribute or not depends on ei and r, altruism, group size, and the probability of being critical to the group outcome. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Distributed envy minimization for resource allocation

Minimizing envy in distributed discrete resource or task allocation, is an unusual distributed optimization challenge, since the quality of the allocation for each of the agents is dependent, not only on its own allocation, but on the allocation of others as well. Thus, in order to perform distributed search for allocations with minimal envy there is a need to design innovative algorithms that can cope with the challenging constraint structure of an envy minimization problem. Distributed methods for minimizing envy among agents in indivisible resource allocation problems are presented. First, Distributed Envy Minimization Problems (DEMP) are formulated as Distributed Constraint Reasoning problems. When the DEMPs are large, and cannot be solved by a complete search an incomplete local search algorithm is presented. Each transfer of a good from one agent to another involves the change of state of more than one agent. Thus, a minimizing envy local search algorithm must build upon actions (transfers) that include multiple agents. Since DEMPs are particularly susceptible to local minima during local search, the paper proposes an algorithm that alternates between two different hill climbing search phases. The first phase uses one-transfer steps while the other exploits envy cycle elimination steps. An algorithm that minimizes envy while preserving efficiency, is proposed. The proposed algorithm finds a Pareto optimal allocation with low envy. In the context of resource allocation problems, a Pareto optimal solution is particularly desirable since it presents a stable solution. The proposed algorithm first finds a divisible Pareto optimal envy-free allocation using a Fisher market equilibrium. This allocation is transferred into an indivisible allocation of goods while maintaining the Pareto optimal characteristic of the allocation and a low envy level among agents.     

Modelling and Solving Employee Timetabling Problems

Employee timetabling is the operation of assigning employees to tasks in a set of shifts during a fixed period of time, typically a week. We present a general definition of employee timetabling problems (ETPs) that captures many real-world problem formulations and includes complex constraints. The proposed model of ETPs can be represented in a tabular form that is both intuitive and efficient for constraint representation and processing. The constraint networks of ETPs include non-binary constraints and are difficult to formulate in terms of simple constraint solvers. We investigate the use of local search techniques for solving ETPs. In particular, we propose several versions of hill-climbing that make use of a novel search space that includes also partial assignments. We show that, on large and difficult instances of real world ETPs, where systematic search fails, local search methods perform well and solve the hardest instances. According to our experimental results on various techniques, a simple version of hill climbing based on random moves is the best method for solving large ETP instances.     

MRI-based Sensors for In Vivo Imaging of Metal Ions in Biology

Although much is known about the diverse roles of metal ions in biology, most of the acquired knowledge was obtained with fluorescent dyes or electrophysiological approaches. However, the ability to non-invasively monitor variation in metal ions and to assess their physiological distribution in health and disease is very limited. Recent advances in the field of molecular magnetic resonance imaging (MRI) have offered new capabilities through the design and development of MRI-responsive sensors for a wide range of applications, including the ability to sense and spatially map metal ions. Here, we briefly summarize the recent progress in the development and performance of MRI sensors designed to monitor metal ions in biology while emphasizing their in vivo uses, their limitations, and remaining challenges. Among the proposed MRI-sensors, Zn²⁺ and Ca²⁺ responsive agents are those that have already been used in live intact subjects, and therefore, these will be emphasized here.     

Evolution of social learning when high expected payoffs are associated with high risk of failure

In an environment where the availability of resources sought by a forager varies greatly, individual foraging is likely to be associated with a high risk of failure. Foragers that learn where the best sources of food are located are likely to develop risk aversion, causing them to avoid the patches that are in fact the best; the result is sub-optimal behaviour. Yet, foragers living in a group may not only learn by themselves, but also by observing others. Using evolutionary agent-based computer simulations of a social foraging game, we show that in an environment where the most productive resources occur with the lowest probability, socially acquired information is strongly favoured over individual experience. While social learning is usually regarded as beneficial because it filters out maladaptive behaviours, the advantage of social learning in a risky environment stems from the fact that it allows risk aversion to be circumvented and the best food source to be revisited despite repeated failures. Our results demonstrate that the consequences of individual risk aversion may be better understood within a social context and suggest one possible explanation for the strong preference for social information over individual experience often observed in both humans and animals.     

On Projection Matrices $$\mathcal{P}^k \to \mathcal{P}^2 ,k = 3,...,6, $$ and their Applications in Computer Vision

Projection matrices from projective spaces have long been used in multiple-view geometry to model the perspective projection created by the pin-hole camera. In this work we introduce higher-dimensional mappings for the representation of various applications in which the world we view is no longer rigid. We also describe the multi-view constraints from these new projection matrices (where k > 3) and methods for extracting the (non-rigid) structure and motion for each application.