The Role of Selectivity in Hierarchical Social Systems

We consider a selection process and a hierarchical institution in a dynamic model as in Harrington [3], where agents are "climbing the pyramid" in a rank-order contest based on the "up or out" policy. Agents are ranked according to the quality of their performances in a particular environment that they face in groups, and a fraction of the highest ranked agents are promoted. The size of this fraction characterizes the selectivity of the process, and we distinguish between local and global selectivity. We study the role of the degree of local and global selectivity in the dynamic process where agents' types differ in their expected performances. Surprisingly, we find that an increase in the selectivity of the process can be detrimental to the agents with the highest expected performances. In fact, it does not matter how small the expected performance of a particular type of agent is. If the degree of selectivity is high enough, that type of agent will survive. However, if the selectivity decreases, the only survivor is the agent with the highest expected performance.Social hierarchy, Selection, Selectivity, Promotion

An Unexpected Role of Local Selectivity in Social Promotion

A selection process and a hierarchical promotion system in a dynamic model are considered as in Harrington (1998) and Garcia-Martinez (2010), where agents are "climbing the pyramid" in a rank-order contest based on the "up or out" policy. The population at any level of the hierarchy is matched in groups of n agents, and each group faces a particular environment. Agents are ranked according to the quality of their performances in each particular environment. The top k performing agents from each group are promoted. The fraction (k/n) characterizes the local selectivity of the process. The role of the degree of local selectivity in the dynamic process where agents' types differ in their expected performances is studied. For low selectivity, the selection process is not strong enough to overcome the inertia of the initial population. If selectivity increases, only the best-performing type of agent will survive. If the selectivity is increased far enough, the worst-performing type also survives, and the proportion for which they account at equilibrium increases as selectivity increases. Therefore, surprisingly, no matter how low the expected success rate of a type is, if the selection process has a high enough level of selectivity, agents of that type survive in the long run: Too much selectivity is always harmful to the best-performing type.Social hierarchy; Selection; Selectivity; Promotion

An Unexpected Role of Local Selectivity in Social Promotion

A selection process and a hierarchical promotion system in a dynamic model are considered as in Harrington (1998) and Garcia-Martinez (2010), where agents are "climbing the pyramid" in a rank-order contest based on the "up or out" policy. The population at any level of the hierarchy is matched in groups of n agents, and each group faces a particular environment. Agents are ranked according to the quality of their performances in each particular environment. The top k performing agents from each group are promoted. The fraction (k/n) characterizes the local selectivity of the process. The role of the degree of local selectivity in the dynamic process where agents' types differ in their expected performances is studied. For low selectivity, the selection process is not strong enough to overcome the inertia of the initial population. If selectivity increases, only the best-performing type of agent will survive. If the selectivity is increased far enough, the worst-performing type also survives, and the proportion for which they account at equilibrium increases as selectivity increases. Therefore, surprisingly, no matter how low the expected success rate of a type is, if the selection process has a high enough level of selectivity, agents of that type survive in the long run: Too much selectivity is always harmful to the best-performing type

The Perverse Incentive of Knowing the Truth

We show that the observation by a principal of the effectiveness of an expert‘s action could induce the expert to lie, damaging the principal. A career-minded expert receives a private-informative signal about the real state of the world, and then he takes an action that can match or not the real state. If a principal observes the consequences of this expert’s action, i.e., if the action matches or not the real state, this expert could disregard his valuable information damaging the principal: the expert plays the opposite action to that recommended by his signal and consequently decreases the probability of matching the real state. However, this expert could play the "recommended" action with positive probability if consequences are not observed. The previous literature has found that "transparency of consequence" can only improves the incentives of the expert to reveal his valuable information. The paradoxical behavior we have found can appear when the expert needs to signal with one action two different kinds of information, and there is a particular "trade-off" in the way of signaling; this "trade-off" can be affected in an unexpected way by the observation of the expert’s action consequences. In this paper, we present a simple model to capture this idea, and characterize the range of the parameters where that occurs

An Unexpected Role of Local Selectivity in Social Promotion

A selection process and a hierarchical promotion system in a dynamic model are considered as in Harrington (1998) and Garcia-Martinez (2010), where agents are "climbing the pyramid" in a rank-order contest based on the "up or out" policy. The population at any level of the hierarchy is matched in groups of n agents, and each group faces a particular environment. Agents are ranked according to the quality of their performances in each particular environment. The top k performing agents from each group are promoted. The fraction (k/n) characterizes the local selectivity of the process. The role of the degree of local selectivity in the dynamic process where agents' types differ in their expected performances is studied. For low selectivity, the selection process is not strong enough to overcome the inertia of the initial population. If selectivity increases, only the best-performing type of agent will survive. If the selectivity is increased far enough, the worst-performing type also survives, and the proportion for which they account at equilibrium increases as selectivity increases. Therefore, surprisingly, no matter how low the expected success rate of a type is, if the selection process has a high enough level of selectivity, agents of that type survive in the long run: Too much selectivity is always harmful to the best-performing type

A method for understanding and digitizing manipulation activities using programming by demonstration in robotic applications

Robots are flexible machines, where the flexibility is achieved, mainly, by the re-programming of the robotic system. To fully exploit the potential of robotic systems, an easy, fast, and intuitive programming methodology is desired. By applying such methodology, robots will be open to a wider audience of potential users (i.e. SMEs, etc.) since the need for a robotic expert in charge of programming the robot will not be needed anymore. This paper presents a Programming by Demonstration approach dealing with high-level tasks taking advantage of the ROS standard. The system identifies the different processes associated to a single-arm human manipulation activity and generates an action plan for future interpretation by the robot. The system is composed of five modules, all of them containerized and interconnected by ROS. Three of these modules are in charge of processing the manipulation data gathered by the sensors system, and converting it from the lowest level to the highest manipulation processes. In order to do this transformation, a module is used to train the system. This module generates, for each operation, an Optimized Multiorder Multivariate Markov Model, that later will be used for the operations recognition and process segmentation. Finally, the fifth module is used to interface and calibrate the system. The system was implemented and tested using a dataglove and a hand position tracker to capture the operator’s data during the manipulation. Four users and five different object types were used to train and test the system both for operations recognition and process segmentation and classification, including also the detection of the locations where the operations are performed.Peer reviewe

Global Positioning System Analysis of Physical Demands in Elite Women’s Beach Handball Players in an Official Spanish Championship

This cross-sectional study aims to analyze the physical demands of elite beach handball players during an official competition. Nine elite female (mean age: 24.6 ± 4.0 years; body weight: 62.4 ± 4.6 kg; body height: 1.68 ± 0.059 m; training experience: 5 years; training: 6 h/week) beach handball players of the Spanish National Team were recruited for this study. A Global Positioning System was incorporated on each player’s back to analyze their movement patterns. Speed and distance were recorded at a sampling frequency of 15 Hz, whereas acceleration was recorded at 100 Hz by means of a built-in triaxial accelerometer. The main finding of the study is that 53% of the distance travelled is done at speeds between 1.5 and 5 km/h and 30% of the distance is between 9 and 13 km/h (83% of the total distance covered), which shows the intermittent efforts that beach handball involves at high intensity, as reflected in the analysis of the internal load with 62.82 ± 14.73% of the game time above 80% of the maximum heart rate. These data help to orientate training objectives to the physical demands required by the competition in order to optimize the players’ performance

Extending the motion planning framework—MoveIt with advanced manipulation functions for industrial applications

MoveIt is the primary software library for motion planning and mobile manipulation in ROS, and it incorporates the latest advances in motion planning, control and perception. However, it is still quite recent, and some important functions to build more advanced manipulation applications, required to robotize many manufacturing processes, have not been developed yet. MoveIt is an open source software, and it relies on the contributions from its community to keep improving and adding new features. Therefore, in this paper, its current state is analyzed to find out which are its main necessities and provide a solution to them. In particular, three gaps of MoveIt are addressed: the automatic tool changing at runtime, the generation of trajectories with full control over the end effector path and speed, and the generation of dual-arm trajectories using different synchronization policies. These functions have been tested with a Motoman SDA10F dual-arm robot, demonstrating their validity in different scenarios. All the developed solutions are generic and robot-agnostic, and they are openly available to be used to extend the capabilities of MoveIt.publishedVersionPeer reviewe

Full Tilt: Universal Constructors for General Shapes with Uniform External Forces

We investigate the problem of assembling general shapes and patterns in a model in which particles move based on uniform external forces until they encounter an obstacle. In this model, corresponding particles may bond when adjacent with one another. Succinctly, this model considers a 2D grid of “open” and “blocked” spaces, along with a set of slidable polyominoes placed at open locations on the board. The board may be tilted in any of the 4 cardinal directions, causing all slidable polyominoes to move maximally in the specified direction until blocked. By successively applying a sequence of such tilts, along with allowing different polyominoes to stick when adjacent, tilt sequences provide a method to reconfigure an initial board configuration so as to assemble a collection of previous separate polyominoes into a larger shape. While previous work within this model of assembly has focused on designing a specific board configuration for the assembly of a specific given shape, we propose the problem of designing universal configurations that are capable of constructing a large class of shapes and patterns. For these constructions, we present the notions of weak and strong universality which indicate the presence of “excess” polyominoes after the shape is constructed. In particular, for given integers h, w, we show that there exists a weakly universal configuration with O(hw) 1 × 1 slidable particles that can be reconfigured to build any h × w patterned rectangle. We then expand this result to show that there exists a weakly universal configuration that can build any h × w-bounded size connected shape. Following these results, which require an admittedly relaxed assembly definition, we go on to show the existence of a strongly universal configuration (no excess particles) which can assemble any shape within a previously studied “drop” class, while using quadratically less space than previous results. Finally, we include a study of the complexity of deciding if a particle within a configuration may be relocated to another position, and deciding if a given configuration may be transformed into a second given configuration. We show both problems to be PSPACE-complete even when no particles stick to one another and movable particles are restricted to 1 × 1 tiles and a single 2 × 2 polyomino