Economic Sociology or Economic Imperialism? The Case of Gary C. Becker

The paper is devoted to a critical analysis of a number of key theories by Gary S. Becker. It is commonly believed that his main accomplishment lies in the extension of the scope of an economic analysis to include numerous traditionally considered as non-economic phenomena. This extension, however, is only feasible at the expense of another extension – this time of the scope of the concepts used. This over-inclusiveness , in turn, makes his theories impossible to falsify, thus calling into question their scientific quality. In the process of considering particular Becker’s conceptions, i.e. human and social capital, the family, marriage and household and the polity a host of other specific drawbacks of Becker’s economic approach to social processes, often related to his ideological bias are indicated.Becker, human capital, social capital, marriage, altruism, self-interest family

Emerging Cooperation in N-Person Iterated Prisoner's Dilemma over Dynamic Complex Networks

The N-Person Iterated Prisoner's Dilemma (NIPD) is an interesting game that has proved to be very useful to explore the emergence of cooperation in multi-player scenarios. Within this game, the way that agents are interconnected is a key element that influences cooperation. In this context, complex networks provide a realistic model of the topological features found in Nature and in many social and technological networks. Considering these networks, it is interesting to study the network evolution, given the possibility that agents can change their neighbors (dynamic rewire), when non-cooperative behaviors are detected. In this paper, we present a model of the NIPD game where a population of genetically-coded agents compete altogether. We analyze how different game parameters, and the network topology, affect the emergence of cooperation in static complex networks. Based on that, we present the main contribution of the paper that concerns the influence of dynamic rewiring in the emergence of cooperation over the NIPD

Percolation breakdown in binary and ternary monodisperse and polydisperse systems of spherical particles

We perform computer simulations of an agglomeration process for monodisperse and polydisperse systems of spherical particles in a cylindrical container, using a simplified stochastic-hydrodynamic model. We consider a ternary system with three particle types A, B, and C, in which only connections of the type can be forged, while any other connections with particles of the same type or with C-particles are forbidden, and for comparison a binary system with two particle types A and C, in which only connections of the type can be formed. We study the breakdown of the percolation in the agglomeration at the bottom of the cylinder with an increasing fraction of C-particles

On-demand QoS and Stability Based Multicast Routing in Mobile Ad Hoc Networks, Journal of Telecommunications and Information Technology, 2014, nr 3

Finding a connection path that remains stable for suciently longer period is critical in mobile ad hoc networks due to frequent link breaks. In this paper, an on-demand Quality of Service (QoS) and stability based multicast routing (OQSMR) scheme is proposed, which is an extension of ad hoc on-demand multicast routing protocol (ODMRP) to provide QoS support for real time applications. The scheme works as follows. Each node in the network periodically estimates the parameters, i.e., node and link stability factor, bandwidth availability, and delays. Next step is creation of neighbor stability and QoS database at every node by using estimated parameters. The last sequence is multicast path construction by using, route request and route reply packets, and QoS and stability information, i.e., link/node stability factor, bandwidth and delays in route information cache of nodes, and performing route maintenance in case of node mobility and route failures. The simulation results indicate that proposed OQSMR demonstrates reduction in packet overhead, improvement in Packet Delivery Ratio (PDR), and reduction in end-to-end delays as compared to ODMRP, and Enhanced ODMRP (E-ODMRP)

Computation offloading for algorithms in absence of the Cloud

Mobile cloud computing is a way of delegating complex algorithms from a mobile device to the cloud to complete the tasks quickly and save energy on the mobile device. However, the cloud may not be available or suitable for helping all the time. For example, in a battlefield scenario, the cloud may not be reachable. This work considers neighbouring devices as alternatives to the cloud for offloading computation and presents three key contributions, namely a comprehensive investigation of the trade-off between computation and communication, Multi-Objective Optimisation based approach to offloading, and Queuing Theory based algorithms that present the benefits of offloading to neighbours. Initially, the states of neighbouring devices are considered to be known and the decision of computation offloading is proposed as a multi-objective optimisation problem. Novel Pareto optimal solutions are proposed. The results on a simulated dataset show up to 30% increment in performance even when cloud computing is not available. However, information about the environment is seldom known completely. In Chapter 5, a realistic environment is considered such as delayed node state information and partially connected sensors. The network of sensors is modelled as a network of queues (Open Jackson network). The offloading problem is posed as minimum cost problem and solved using Linear solvers. In addition to the simulated dataset, the proposed solution is tested on a real computer vision dataset. The experiments on the random waypoint dataset showed up to 33% boost on performance whereas in the real dataset, exploiting the temporal and spatial distribution of the targets, a significantly higher increment in performance is achieved

An Overview of Catastrophic AI Risks

Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose catastrophic risks. Although numerous risks have been detailed separately, there is a pressing need for a systematic discussion and illustration of the potential dangers to better inform efforts to mitigate them. This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories: malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs; organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents; and rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans. For each category of risk, we describe specific hazards, present illustrative stories, envision ideal scenarios, and propose practical suggestions for mitigating these dangers. Our goal is to foster a comprehensive understanding of these risks and inspire collective and proactive efforts to ensure that AIs are developed and deployed in a safe manner. Ultimately, we hope this will allow us to realize the benefits of this powerful technology while minimizing the potential for catastrophic outcomes

Enhancing Node Cooperation in Mobile Wireless Ad Hoc Networks with Selfish Nodes

In Mobile Ad Hoc Networks (MANETs), nodes depend on each other for routing and forwarding packets. However, to save power and other resources, nodes belonging to independent authorities may behave selfishly, and may not be willing to help other nodes. Such selfish behavior poses a real threat to the proper functioning of MANETs. One way to foster node cooperation is to introduce punishment for selfish nodes. Based on neighbor-monitoring techniques, a fully distributed solution to detect, punish, and re-admit selfish nodes, is proposed here. This solution provides nodes the same opportunity to serve/and be served by others. A light-weight solution regarding battery status is also proposed here. This solution requires neighbor monitoring only when necessary, thereby saving nodes battery power. Another effective way to solve the selfish-node problem is to reward nodes for their service according to their cost. To force nodes to show their true cost, truthful protocols are needed. A low overhead truthful routing protocol to find optimal routes is proposed in this thesis. The most prominent feature of this protocol is the reduction of overhead from existing solutions O(n3) to O(n2). A light-weight scalable truthful routing protocol (LSTOP) is further proposed, which finds near-least-cost paths in dense networks. LSTOP reduces overhead to O(n) on average, and O(n2) in worst case scenarios. Multiple path routing protocols are an effective alternative to single path routing protocols. A generic mechanism that can turn any table-driven multipath routing protocol into a truthful one, is outlined here. A truthful multipath routing protocol (TMRP), based on well-known AOMDV protocol, is presented as an example. TMRP incurs an only 2n message overhead for a route discovery, and can also achieve load balancing without compromising truthfulness. To cope with the selfish-node problem in the area of position-based routing, a truthful geographic forwarding (TGF) algorithm is presented. TGF utilizes three auction-based forwarding schemes to stimulate node cooperation. The truthfulness of these schemes is proven, and their performance is evaluated through statistical analysis and simulation studies