Agent-based Computer Research Articles

Agent-Based Fog Computing: Gossiping, Reasoning, and Bargaining

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fog computing</i> is a layered model that consists of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fog nodes</i> residing between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cloud data centers</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Internet of Things</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoT</i> ) devices. With a large number of highly distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fog nodes</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IoT</i> devices, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized and computationally efficient techniques </i> for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resource discovery and selection</i> are necessities of fog computing. Since consumers are charged for using fog and cloud resources, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">economically efficient resource pricing mechanisms</i> are crucial. This work devises: 1) an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agent-based fog computing model</i> , 2) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gossip-based resource discovery algorithm</i> for propagating resource requests, 3) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reasoning technique</i> for determining similarities between user requirements and resource profiles, and 4) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bargaining mechanism</i> for dynamically pricing vertically arranged fog and cloud resources. Mathematical proofs validate that the gossip algorithm and reasoning technique are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computationally efficient</i> , and the bargaining mechanism is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">economically efficient</i> . Since agents can determine resource similarities <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">independently</i> and propagate requests <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">autonomously</i> without centralized control, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agent-based resource discovery and selection approaches are decentralized techniques</i> .

Computer Simulations of Ethics: the Applicability of Agent-Based Modeling for Ethical Theories

I consider the applicability of Agent-Based Modeling (ABM) and computer simulations for ethical theories. Though agent-based modeling is already well established in the social sciences, it has not yet found acceptance in the field of philosophical ethics. Currently, there are only a few works explicitly connecting ethics with agent-based modeling. In this paper, I show that it is possible to build computer simulations of ethical theories and that there are also potential benefits in doing so: (1) the opportunity for virtual ethical experiments that are impossible to do in real life, and (2) an increased understanding and appreciation of an ethical theory either through the programming implementation or through the visual simulation. In the first part of the paper, I mention some social science simulations with ethical import that could encourage ethicists to work with ABM. Second, I list the few pioneering works that attempt to combine computer simulation with philosophical ethics, the most prominent being Evolving Ethics: The New Science of Good and Evil (2010) by Mascaro et al. Third, I give pointers for the computer simulation of the most prominent ethical theories: deontological ethics, utilitarianism, feminist care ethics, and virtue ethics. In the final part, I consider the potential of using an existing reference model for the simulation of human behavior, the PECS model, as the foundation for a computer simulation of virtue ethics.

Application of agent-based paradigm to model corrosion of steel in concrete environment

ABSTRACTThis paper explains developing an agent-based computer model to predict the corrosion of steel bars in the high alkaline environment in concrete. Modelling started with specific assumptions about ‘agents’, how they interact and how the population changes over time. A simulation approach, employing agent-based modelling, was formulated and corrosion of steel in the alkaline environment of concrete pore solution, with different chloride concentrations, was simulated. The electrochemical behaviour of steel in a chloride-contaminated concrete pore solution was also experimentally examined. Results of the simulations and the experiments were in good agreement.

Using an agent-based model to evaluate the effect of producer specialization on the epidemiological resilience of livestock production networks.

An agent-based computer model that builds representative regional U.S. hog production networks was developed and employed to assess the potential impact of the ongoing trend towards increased producer specialization upon network-level resilience to catastrophic disease outbreaks. Empirical analyses suggest that the spatial distribution and connectivity patterns of contact networks often predict epidemic spreading dynamics. Our model heuristically generates realistic systems composed of hog producer, feed mill, and slaughter plant agents. Network edges are added during each run as agents exchange livestock and feed. The heuristics governing agents’ contact patterns account for factors including their industry roles, physical proximities, and the age of their livestock. In each run, an infection is introduced, and may spread according to probabilities associated with the various modes of contact. For each of three treatments—defined by one-phase, two-phase, and three-phase production systems—a parameter variation experiment examines the impact of the spatial density of producer agents in the system upon the length and size of disease outbreaks. Resulting data show phase transitions whereby, above some density threshold, systemic outbreaks become possible, echoing findings from percolation theory. Data analysis reveals that multi-phase production systems are vulnerable to catastrophic outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outbreak scales and durations. Key differences in network-level metrics shed light on these results, suggesting that the absence of potentially-bridging producer–producer edges may be largely responsible for the superior disease resilience of single-phase “farrow to finish” production systems.

The role of a "common is moral" heuristic in the stability and change of moral norms.

Moral norms are fundamental for virtually all social interactions, including cooperation. Moral norms develop and change, but the mechanisms underlying when, and how, such changes occur are not well-described by theories of moral psychology. We tested, and confirmed, the hypothesis that the commonness of an observed behavior consistently influences its moral status, which we refer to as the common is moral (CIM) heuristic. In 9 experiments, we used an experimental model of dynamic social interaction that manipulated the commonness of altruistic and selfish behaviors to examine the change of peoples' moral judgments. We found that both altruistic and selfish behaviors were judged as more moral, and less deserving of punishment, when common than when rare, which could be explained by a classical formal model (social impact theory) of behavioral conformity. Furthermore, judgments of common versus rare behaviors were faster, indicating that they were computationally more efficient. Finally, we used agent-based computer simulations to investigate the endogenous population dynamics predicted to emerge if individuals use the CIM heuristic, and found that the CIM heuristic is sufficient for producing 2 hallmarks of real moral norms; stability and sudden changes. Our results demonstrate that commonness shapes our moral psychology through mechanisms similar to behavioral conformity with wide implications for understanding the stability and change of moral norms. (PsycINFO Database Record

Using Collaborative Agent-Based Computer Modeling to Explore Tri-Trophic Cascades with Elementary School Science Students

This paper investigates an in-service teacher and her student’s abilities to utilize, implement, and enact a participatory agent-based modeling program, developed as part of the group-based cloud computing (GbCC) for STEM Education Project funded by the National Science Foundation. In this first cycle of design-based implementation research with an in-service teacher and her 300 students, we examine student participatory learning and teacher experience. By implementing models with teachers, we intend to 1) improve iteratively the GbCC learning technologies and 2) develop more informed and aligned pedagogies for teaching in socially mediated and generative learning environments.

Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?

This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students’ reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.

A conceptual and computational framework for modelling and understanding the non-equilibrium gene regulatory networks of mouse embryonic stem cells.

The capacity of pluripotent embryonic stem cells to differentiate into any cell type in the body makes them invaluable in the field of regenerative medicine. However, because of the complexity of both the core pluripotency network and the process of cell fate computation it is not yet possible to control the fate of stem cells. We present a theoretical model of stem cell fate computation that is based on Halley and Winkler’s Branching Process Theory (BPT) and on Greaves et al.’s agent-based computer simulation derived from that theoretical model. BPT abstracts the complex production and action of a Transcription Factor (TF) into a single critical branching process that may dissipate, maintain, or become supercritical. Here we take the single TF model and extend it to multiple interacting TFs, and build an agent-based simulation of multiple TFs to investigate the dynamics of such coupled systems. We have developed the simulation and the theoretical model together, in an iterative manner, with the aim of obtaining a deeper understanding of stem cell fate computation, in order to influence experimental efforts, which may in turn influence the outcome of cellular differentiation. The model used is an example of self-organization and could be more widely applicable to the modelling of other complex systems. The simulation based on this model, though currently limited in scope in terms of the biology it represents, supports the utility of the Halley and Winkler branching process model in describing the behaviour of stem cell gene regulatory networks. Our simulation demonstrates three key features: (i) the existence of a critical value of the branching process parameter, dependent on the details of the cistrome in question; (ii) the ability of an active cistrome to “ignite” an otherwise fully dissipated cistrome, and drive it to criticality; (iii) how coupling cistromes together can reduce their critical branching parameter values needed to drive them to criticality.

Uncommon exit arrangement effects in airplane emergency evacuation

This paper presents the effects of uncommon exit size and location arrangement in the emergency evacuation of transport airplanes. The analysis is carried out by means of an agent-based computer model conceived to simulate the evacuation of narrow-body aircraft as required in the certification process, as well as for design purposes. The simulation model provides full evacuation data of all occupants: escape route followed, distance to exit, time to reach the ground, etc., as well as those of the whole cabin: exit utilization patterns, evacuation histograms, chronolines and total evacuation time. The present research concentrates on the effect of uncommon exit size and location arrangements, such as large longitudinal shifting and/or suppression of some of the exits in the evacuation performance.

Modelling the spread of innovation in wild birds.

We apply three plausible algorithms in agent-based computer simulations to recent experiments on social learning in wild birds. Although some of the phenomena are simulated by all three learning algorithms, several manifestations of social conformity bias are simulated by only the approximate majority (AM) algorithm, which has roots in chemistry, molecular biology and theoretical computer science. The simulations generate testable predictions and provide several explanatory insights into the diffusion of innovation through a population. The AM algorithm's success raises the possibility of its usefulness in studying group dynamics more generally, in several different scientific domains. Our differential-equation model matches simulation results and provides mathematical insights into the dynamics of these algorithms.

Designs for learning about climate change as a complex system

This paper reports on a study in which students used agent-based computer models to learn about complex systems ideas of relevance to understanding climate change. The experimental condition used a Productive Failure (PF) learning design in which ninth grade students initially worked with agent-based computer models to solve challenge problems followed by teacher instruction about targeted climate and complexity ideas. In contrast, the comparison condition employed a Direct Instruction (DI) learning design in which the teacher instruction was provided initially, followed by the students working on the same computer models and challenge problems as the experimental group. The students in the PF group scored significantly higher on the post-test on measures of climate and complex systems explanatory knowledge and near and far knowledge transfer. Theoretical and practical implications of these findings are considered.

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, ​we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.

The Role of Cell Adhesion in the Malaria Life Cycle: From Gliding Sporozoites to Rolling Adhesion of Infected Red Blood Cells

During its journey through the human host, the malaria parasite makes strong use of adhesive interactions with its environment. We use biophysical assays and mathematical models to investigate the underlying mechanisms. After release into the host skin during a mosquito blood meal, the malaria sporozoite quickly moves through the connective tissue using gliding motility. On a flat substrate, single cells follow circular trajectories with stick-and-slip motion. Using pillar arrays and agent-based computer simulations, we show how this circular motion is converted into different motion patterns that depend on the geometrical properties of the environment. During the blood stage, malaria parasites rebuild the infected red blood cells so that they start adhering to the vascular endothelium, in order to avoid clearance by the spleen. Using flow chamber experiments and adhesive dynamics simulations, we show how stable rolling adhesion in shear flow emerges roughly at the transition from the trophozoite to the schizont stage.

Simulating the Formation and Dynamics of the Implicit Attitude; a Social Cognition Study

The current study aimed to define some factors contributing to implicit attitude formation mainly in the social interaction context. An agent-based computer simulation of a society including autonomous agents and an attitude object was used to track the implicit attitude progress towards the object. The society could simulate the autonomic behaviors. We provided a complex adaptive system and observed an emergent phenomenon as the formation and dynamics of implicit attitude in the society. Our results suggested that population size and the number of high-impact individuals are important for the formation of implicit attitude in a society. Moreover, when the number of factors affecting agents’ relationships increases, the dynamics of society tended to unpredictability. Our experience showed that diverse autonomous components of a society with implemented simple rules lead to emergent and seemingly organized system behavior, and the pattern of behavior can be affected by communication and environmental stress. Our study attempted to offer some key implications since few theories within the cognitive psychology and sociology have been stated in precise and unambiguous terms.

Use of a sonification system for science learning by people who are blind

Purpose The purpose of this paper is to examine a central need of students who are blind: the ability to access science curriculum content. Design/methodology/approach Agent-based modeling is a relatively new computational modeling paradigm that models complex dynamic systems. NetLogo is a widely used agent-based modeling language that enables exploration and construction of models of complex systems by programming and running the rules and behaviors. Sonification of variables and events in an agent-based NetLogo computer model of gas in a container is used to convey phenomena information. This study examined mainly two research topics: the scientific conceptual knowledge and systems reasoning that were learned as a result of interaction with the listen-to-complexity (L2C) environment as appeared in answers to the pre- and post-tests and the learning topics of kinetic molecular theory of gas in chemistry that was learned as a result of interaction with the L2C environment. The case study research focused on A., a woman who is adventitiously blind, for eight sessions. Findings The participant successfully completed all curricular assignments; her scientific conceptual knowledge and systems reasoning became more specific and aligned with scientific knowledge. Practical implications A practical implication of further studies is that they are likely to have an impact on the accessibility of learning materials, especially in science education for students who are blind, as equal access to low-cost learning environments that are equivalent to those used by sighted users would support their inclusion in the K-12 academic curriculum. Originality/value The innovative and low-cost learning system that is used in this research is based on transmittal of visual information of dynamic and complex systems, providing perceptual compensation by harnessing auditory feedback. For the first time the L2C system is based on sound that represents a dynamic rather than a static array. In this study, the authors explore how a combination of several auditory representations may affect cognitive learning ability.

Robustness of linkage strategy that leads to large-scale cooperation

One of the most well-known models to characterize cooperation among unrelated individuals is Social dilemma (SD). However there is no consensus about how to solve the SD by itself. Since SDs are often embedded in other social interactions, including indirect reciprocity games (IR), human can coordinate their behaviors across multiple games. Such coordination is called ‘linkage’. Recently linkage has been considered as a promising solution to resolve SDs, since excluding SD defectors (i.e. those who defected in SD) from indirectly reciprocal relationships functions as a costless sanction. A previous study performed mathematical modeling and revealed that a linkage strategy, which cooperates in SD and engages in the Standing strategy in IR based on the recipients' behaviors in both SD and IR, was an ESS against a non-linkage strategy which defects in SD and engages in the Standing strategy in IR based on recipients' behaviors only in IR (Panchanathan and Boyd, 2004). In order to investigate the robustness of the linkage strategy, we devised a non-linkage strategy, which cooperates in SD but does not link two games. First, we conducted a mathematical analysis and demonstrated that the linkage strategy was not an ESS against cooperating non-linkage strategy. Second, we conducted a series of agent-based computer simulations to examine how the strategies perform in situations in which various types of errors can occur. Our results showed that the linkage strategy was an ESS only when there are implementation errors in SD. However, the equilibrium of the linkage strategy was unstable when there are perception errors. Since we know that humans are not free from perception errors in their social life, future studies will need to show how perception errors can be overcome in order to provide support for the conclusion that linkage is a plausible solution to SDs.

Number of Parties, Endogenous Partisan Preferences and Electoral Turnout Rates: A Stochastic Equilibrium Analysis

This paper analyzes the impact of the number of parties on turnover rates within an endogenous partisan preference framework. Our results show that the turnover increases in the number of parties under various levels of individual propensity to abstain. We find strong interactions effects between both motives for abstention, alienation and indifference, and the abstention propensity. The results are based on an agent-based computer simulation of multi-party electoral competition in a two-dimensional policy space.

A low-level resource allocation in an agent-based Cloud Computing platform

The distribution of computational resources in a Cloud Computing platform is a complex process with several parameters to consider such as the demand for services, available computational resources and service level agreements with end users. Currently, the state-of-the-art presents centralized approaches derived from previous technologies related to cluster of servers. These approaches allocate computational resources by means of the addition/removal of (physical/virtual) computational nodes. However, virtualization technology currently allows for research into new techniques, which makes it possible to allocate at a lower level. In other words, not only is it possible to add/remove nodes, but also to modify the resources of each virtual machine (low level resource allocation). Thus, agent theory is a key technology in this field, allowing decentralized resource allocation. This innovative approach has undeniable improvements such us computational load distribution and reduced computation time. The evaluation was carried out through experiments in a real Cloud environment, thus proving the validity of the proposed approach.

An urban form response to disaster vulnerability: Improving tsunami evacuation in Iquique, Chile

As urbanization gathers pace and climate change increases the number and magnitude of many natural hazards, cities are increasingly becoming hot spots for disasters. Although the role of appropriate urban forms in reducing disaster vulnerability has been recognized for some time, the majority of its potential remains focused on long-term mitigation efforts. In contrast, examination of the relationships with short-term disaster management activities such as response and immediate recovery has not been thoroughly conducted. This paper contributes to this shortfall by analysing a critical type of rapid onset disaster, a near-field tsunami, and the role of urban form in supporting the populations’ core response activities of evacuation and sheltering. The Chilean city of Iquique (affected by a severe earthquake and minor tsunami in 2014) is examined using a mixed methods approach that provides the basis for proposed macro-scale and micro-scale changes in its urban form; these modifications, in turn, are assessed with geographic information system (GIS) and agent-based computer models. The results show important existing evacuation vulnerability throughout major areas of the city (as the result of interrelated critical conditions), which nonetheless could be significantly reduced by the changes proposed. Further steps in this iterative process, in turn, could lead to the development of evacuation-based urban design standards capable of being transferred to different tsunami-prone contexts around the world.

Monte Carlo simulation of body height in a spatial network.

BACKGROUND/OBJECTIVES Recent evidence suggests clustering of human body height. We want to assess the consequences of connectedness in a spatial network on height clustering in an artificial society. SUBJECTS/METHODS We used an agent-based computer modelling technique (Monte Carlo simulation) and compared simulated height in a spatial network with characteristics of the observed geographic height distribution of three historic cohorts of Swiss military conscripts (conscripted in 1884-1891; 1908-1910; and 2004-2009). RESULTS Conscript height shows several characteristic features: (1) height distributions are overdispersed. (2) Conscripts from districts with direct inter-district road connections tend to be similar in height. (3) Clusters of tall and clusters of short stature districts vary over time. Autocorrelations in height between late 19th and early 21st century districts are low. (4) Mean district height depends on the number of connecting roads and on the number of conscripts per district. Using Monte Carlo simulation, we were able to generate these natural characteristics in an artificial society. Already 5% height information from directly connected districts is sufficient to simulate the characteristics of natural height distribution. Very similar observations in regular rectangular networks indicate that the characteristics of Swiss conscript height distributions do not so much result from the particular Swiss geography but rather appear to be general features of spatial networks. CONCLUSIONS Spatial connectedness can affect height clustering in an artificial society, similar to that seen in natural cohorts of military conscripts, and strengthen the concept of connectedness being involved in the regulation of human height.