Norms are known to be a major factor determining humans behavior. It's also shown that norms can be quite effective tool for building agent-based societies. Various normative architectures have been proposed for designing normative multi-agent systems (NorMAS). Due to human nature of the concept norms, many of these architectures are built based on theories in social sciences. Tipping point theory, as is briefly discussed in this paper, seems to have a great potential to be used for designing normative architectures. This theory deals with the factors that affect social epidemics that arise in human societies. In this paper, we try to apply the main concepts of this theory to agent-based normative architectures. We show several ways to implement these concepts, and study their effects in an agent-based normative scenario.
Hollander and Wu @cite_8 refer to three categories of normative studies in the social sciences: 1) social function of norms @cite_31 , 2) impact of social norms @cite_7 , 3) mechanisms leading to the emergence and creation of norms @cite_35 . In the context of social function, norms are often concerned with the and of agent behavior; where oughtness refers to the condition where an agent should or should not perform an action, and expectation refers to the behavior that other agents expect to observe from that agent @cite_37 @cite_9 . An example of work belonging to this category is Boella and Torre's architecture containing separate subsystems for counts-as conditionals, conditional obligations, and conditional permissions @cite_15 .
@cite_35 @cite_37 @cite_7 @cite_8 @cite_15 @cite_9 @cite_31
2017492123 1827387789 1567484702 2133901799 2067743023 2237315243
Urban simulations are an important tool for analyzing many policy questions relating to the usage of public space, roads, and communal transportation; they can be used to predict the long-term impact of new construction projects, traffic restrictions, and zoning laws. However, it is unwise to rely upon predictions from a single model since each technique possesses different strengths and weaknesses and can be highly sensitive to the choice of parameters and initial conditions. In this article, we describe a hybrid approach for combining agent-based and stochastic simulations (Markov chain Monte Carlo, MCMC) to improve the accuracy and reduce the variance of long-term predictions. In our proposed approach, the agent-based model is used to bootstrap the proposal distribution for the MCMC estimator. To demonstrate the applicability of our modeling technique, this article presents a case study describing the usage of our hybrid simulation method for forecasting transportation patterns and parking lot utilization on a large university campus. A comparison of our simulation results against an independently collected dataset reveals that our hybrid approach accurately predicts parking lot usage and performs significantly better than other comparable modeling techniques. Developing novel architectures for combining the predictions of agent-based models can produce insights that are different than simply selecting the best model. The Markov Chain Monte Carlo (MCMC) family of methods form a valuable part of the toolbox of social modeling and prediction techniques, enabling modelers to generate samples and summary statistics of a population of interest with minimal information. It has been used successfully to model changes over time in many types of social systems, including patterns of disease spread, adolescent smoking, and geopolitical conflicts. In MCMC an initial proposal distribution is iteratively refined until it approximates the posterior distribution. However, the selection of the proposal distribution can have a significant impact on model convergence. In this paper, we propose a new hybrid modeling technique in which an agent-based model is used to initialize the proposal distribution of the MCMC simulation. We demonstrate the use of our modeling technique in an urban transportation prediction scenario and show that the hybrid combined model produces more accurate predictions than either of the parent models. Holonic multi-agent systems are a special category of multi-agent systems that best fit to environments with numerous agents and high complexity. Like in general multi-agent systems, the agents in the holonic system may negotiate with each other. These systems have their own characteristics and structure, for which a specific negotiation mechanism is required. This mechanism should be simple, fast and operable in real world applications. It would be better to equip negotiators with a learning method which can efficiently use the available information. The learning method should itself be fast, too. Additionally, this mechanism should match the special characteristics of the holonic multi-agent systems. In this paper, we introduce such a negotiation method. Experimental results demonstrate the efficiency of this new approach. Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research. Normative systems are traditionally described and analyzed using deontic logic, describing the logical relations among obligations and permissions. However, there is still a gap between deontic logic and normative multi-agent systems such as electronic institutions, which may be seen as an instance of the gap between on the one hand logical agent specification languages and on the other hand agent architectures and programming languages. To bridge the gap, in this paper we propose an architecture containing separate subsystems or components for counts-as conditionals, conditional obligations and conditional permissions. We add a norm database component in which the three kinds of rules are stored, and we use a channel based coordination model to describe the relations among the four normative components. Norms are an important part of human social systems, governing many aspects of group decision-making. Yet many popularly used social models neglect to model normative effects on human behavior, relying on simple probabilistic and majority voting models of influence diffusion. Within the multi-agent research community, the study of norm emergence, compliance, and adoption has resulted in new architectures and standards for normative agents; however few of these models have been successfully applied to real-world public policy problems. In this paper, we propose a new lightweight architecture for constructing normative agents to model human social systems; the aim of our research is to be able to study the effects of different public policy decisions on a community. Here we present a case study showing the usage of our architecture for predicting trends in smoking cessation resulting from a smoke-free campus initiative. Our agent-based model combines social, environmental, and personal factors to accurately predict smoking trends and attitudes. The performance of both the whole and ablated model is evaluated against statistics from an independent source.