Agent-Based Modeling for Efficiency Policy of Households\u27 Electricity Consumption in Bandung City

There are many argumentations that the government subsidy in electricity, for holding down the electricity fares, has encouraged extravagant use of electricity. Therefore, the Government Budget (APBN) Burden for this subsidy becomes higher than it necessary. Some experts have argued that this money can actually be used for other necessary and urgent spending such as infrastructure development. The purpose of this research is to build an agent-based model and simulation to explore the dinamic of electricity consumption in Indonesia, especially in Bandung City. Several interviews were conducted about households\u27 behaviors in consuming electricity to build the model. Then, the parameter was set based on the data collected with questionaire and secondary data source. The model was tested for its validity and sensitivity to the change in the controlled parameter. The simulation is expected to provide the government with a combination of electricity fare to various households electricity market segments to ensure an economic use of electricity. Some scenarios were run and the emergent property, the whole system electricity consumption,were observed to find out the best policy to encourage efficient use of electricity in the system

SQUAD GAME: AGENT BASED MODELING IMPLEMENTATION

ABSTRAKSI: -Kata Kunci : -ABSTRACT: Most of Game Theories and Artificial Intelligences nowadays cover the characters in the game as individual not a team. Whereas, there are several games with team work needed for running the characters.This final essay covers the development on a MAS (Multi-agent System) to control the behavior of characters in Squad Game. The system design by using Prometheus Methodology as the model, and Q-Learning as the learning method. This system is designed by using Prometheus Design Tool (PDT) that will generate the JACK code, which is transformed to C# programming language.Overall, up to 80% of the tests in all scenarios succes to solve all problems faced in the game.Keyword: agent, multi-agent system, prometheus, q-learning, observer pattern

Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

Agent-Based Modeling of Intracellular Transport

We develop an agent-based model of the motion and pattern formation of vesicles. These intracellular particles can be found in four different modes of (undirected and directed) motion and can fuse with other vesicles. While the size of vesicles follows a log-normal distribution that changes over time due to fusion processes, their spatial distribution gives rise to distinct patterns. Their occurrence depends on the concentration of proteins which are synthesized based on the transcriptional activities of some genes. Hence, differences in these spatio-temporal vesicle patterns allow indirect conclusions about the (unknown) impact of these genes. By means of agent-based computer simulations we are able to reproduce such patterns on real temporal and spatial scales. Our modeling approach is based on Brownian agents with an internal degree of freedom, $\theta$, that represents the different modes of motion. Conditions inside the cell are modeled by an effective potential that differs for agents dependent on their value $\theta$. Agent's motion in this effective potential is modeled by an overdampted Langevin equation, changes of $\theta$ are modeled as stochastic transitions with values obtained from experiments, and fusion events are modeled as space-dependent stochastic transitions. Our results for the spatio-temporal vesicle patterns can be used for a statistical comparison with experiments. We also derive hypotheses of how the silencing of some genes may affect the intracellular transport, and point to generalizations of the model

Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

Agent-Based Modeling of Pollen Competition

Non-random mating in Arabidopsis Thaliana is, at least in part, due to intense competition between pollen grains to fertilize the limited number of ovules. Previous studies have pinpointed some of the competition traits that make pollen more or less competitive. Using these competition traits, we will build an agent-based computer model with NetLogo that simulates the competition between two accessions of Arabidopsis Thaliana pollen. This 2D model will allow the user to adjust pollen traits and competition strategies for each of the two pollen accessions. Some of the factors being considered include pollen viability, pollen tube growth rate, nutrients provided by the female, pollen tube attrition and the means of locating unfertilized ovules. To assess the competitiveness of the selected pollen traits, this model will track the number of fertilized ovules and maximum pollen tube length for each accession. This agent-based model will allow further study into the traits that make pollen most competitive as well as the strategies used by pollen to fertilize ovules. This model has the potential to quickly test a wide variety of competition traits and strategies without the need for in-lab experiments

The ABM Template Models -- A Reformulation with Reference Implementations

This paper refines a well-known set of template models for agent-based modeling and offers new reference implementations. It also addresses issues of design, flexibility, and ease of use that are relevant to the choice of an agent-based modeling platform.

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update

Agent-Based Modeling of the Prediction Markets

We propose a simple agent-based model of the political election prediction market which reflects the intrinsic feature of the prediction market as an information aggregation mechanism. Each agent has a vote, and all agents’ votes determine the election result. Some of the agents participate in the prediction market. Agents form their beliefs by observing their neighbors’ voting disposition, and trade with these beliefs by following some forms of the zero-intelligence strategy. In this model, the mean price of the market is used as a forecast of the election result. We study the effect of the radius of agents’ neighborhood and the geographical distribution of information on the prediction accuracy. In addition, we also identify one of the mechanisms which can replicate the favorite-longshot bias, a stylized fact in the prediction market. This model can then provide a framework for further analysis on the prediction market when market participants have more sophisticated trading behavior.Prediction market, Agent-based simulation, Information aggregation mechanism, Prediction accuracy, Zero-intelligence agents, Favorite-longshot bias