Recent progress on formal and computational model for A. Smiths Invisible Hand paradigm

The recent economic crisis has boosted a very strong demand for quite new tools to analyze and predict the behavior of quasi-free1 markets. The paper presents our effort to build a formal theory of A. Smith's Invisible Hand [5] paradigm (ASIH) and simulation model for a selected case. It proves that ASIH is not only an economic idea, which conflict on ways to govern [16], but something that really exists, for which formal a theory can be built. Moreover, ASIH can be measured [17], and in the future probably utilized for quasi-free market analysis and prediction. In advance, we want to state, that ASIH according to our theory, can generate both correct and incorrect decisions. For this, we use the theory of computational Collective Intelligence [18] and a molecular model of computations [1], [2]. Our theory assumes that ASIH is an unconscious meta-inference process spread on the platform of brains of agents. This meta-process is: distributed, parallel, and non-deterministic, and is run on a computational platform of market agents' brains. The ASIH inference process emerges spontaneously in certain circumstances and can vanish when market situation changes. Since the ASIH platform is made up of brains of agents, conclusions of this inference process affect the behavior of agents and therefore the behavior of the entire market. Our research unveils that ASIH is in fact a family of similar meta-processes; thus ASIHs for different economic eras are different because corresponding models of brains of market agents are different. The paper will present and explain, on the basis of a simulation model, a case of powerful ASIH response at the end of the 15th century due to a blockade (taxes and the Dardanelles sea-route cutoff) of spice trade by Turks and Arabs. ASIH also responded to the discovery of America, the emergence of a sailing route around Africa, the establishment of plantations (sugarcane, spices) and modern galleons2 technology. This case demonstrates how powerful and with far-reaching consequences, ASIH can b

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

On the Performance of Swarm Intelligence Optimization Algorithms for Phase Stability and Liquid-Liquid and Vapor-Liquid Equilibrium Calculations

This study introduces new soft computing optimization techniques for performing the phase stability analysis and phase equilibrium calculations in both reactive and non-reactive systems. In particular, the performance of the several swarm intelligence optimization methods is compared and discussed based on both reliability and computational efficiency using practical stopping criteria for these applied thermodynamic calculations.  These algorithms are: Intelligent Firefly Algorithm (IFA), Cuckoo Search (CS), Artificial Bee Algorithm (ABC) and Bat Algorithm (BA). It is important to note that no attempts have been reported in the literature to evaluate their performance in solving the phase and chemical equilibrium problems. Results indicated that CS was found to be the most reliable technique across different problems tried at the time that it requires similar computational effort to the other methods. In summary, this study provides new results and insights about the capabilities and limitations of bio-inspired optimization methods for performing applied thermodynamic calculations

A Novel Approach to Finding Near-Cliques: The Triangle-Densest Subgraph Problem

Many graph mining applications rely on detecting subgraphs which are near-cliques. There exists a dichotomy between the results in the existing work related to this problem: on the one hand the densest subgraph problem (DSP) which maximizes the average degree over all subgraphs is solvable in polynomial time but for many networks fails to find subgraphs which are near-cliques. On the other hand, formulations that are geared towards finding near-cliques are NP-hard and frequently inapproximable due to connections with the Maximum Clique problem. In this work, we propose a formulation which combines the best of both worlds: it is solvable in polynomial time and finds near-cliques when the DSP fails. Surprisingly, our formulation is a simple variation of the DSP. Specifically, we define the triangle densest subgraph problem (TDSP): given $G(V,E)$, find a subset of vertices $S^*$ such that $\tau(S^*)=\max_{S \subseteq V} \frac{t(S)}{|S|}$, where $t(S)$ is the number of triangles induced by the set $S$. We provide various exact and approximation algorithms which the solve the TDSP efficiently. Furthermore, we show how our algorithms adapt to the more general problem of maximizing the $k$-clique average density. Finally, we provide empirical evidence that the TDSP should be used whenever the output of the DSP fails to output a near-clique.Comment: 42 page