Translation of string-and-pin-based shortest path construction into data-scalable agent-based computational models

Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking) and storage requirements for different capture configurations.

Multi-agent simulations are useful for exploring collective patterns of individual behavior in social, biological, economic, network, and physical systems. However, there is no provenance support for multi-agent models (MAMs) in a distributed setting. To this end, we introduce ProvMASS, a novel approach to capture provenance of MAMs in a distributed memory by combining inter-process identification, lightweight coordination of in-memory provenance storage, and adaptive provenance capture. ProvMASS is built on top of the Multi-Agent Spatial Simulation (MASS) library, a framework that combines multi-agent systems with large-scale fine-grained agent-based models, or MAMs. Unlike other environments supporting MAMs, MASS parallelizes simulations with distributed memory, where agents and spatial data are shared application resources. We evaluate our approach with provenance queries to support three use cases and performance measures. Initial results indicate that our approach can support various provenance queries for MAMs at reasonable performance overhead.

One of the major trends in traffic simulations is to take into account microscopic aspects of traffic flows at the street level. Multi-agent models such as MATSim (multi-agent transport simulation) have been highlighted for recent years as a solution to address these complex and microscopic simulation requirements. They are viewed as an emergent and collective behavior of agents, (i.e., vehicles). However, as the simulations scale up, their computational requirements could get increased beyond the capability of a single CPU and thus should be fulfilled with parallelization. Multithreading can partially contribute to parallelization by utilizing multi-cores, but cannot give full scalability of both CPU power and memory space. To support distributed-memory parallelization for multi-agent models, we have developed the MASS (multi-agent spatial simulation) library. This paper presents how to parallelize MATSim using the MASS library and demonstrates the library's portability and execution performance in practical transport simulations.

The realisation of food security is hindered by its inherently complex nature. Within less developed countries, a number of contributing factors act to undermine food security. At the level of rural households, livelihoods remain largely dependent upon agriculture. Structural factors such as small plot sizes, widespread soil degradation and a continued dependence upon rain fed agriculture, leave smallholders vulnerable to climatic shocks. A key objective of the project was to explore the potential role of simulation tools in understanding the complexity of sustainable food provision. The use of simulation techniques can act as a tool to explore the complex social, ecological and political factors affecting food availability, access and utilisation. Agent-based modelling (ABM) is one possible technique, which comprises a computerised simulation of agents located within an environment. Each agent provides a computational representation of a real-world actor, such as an individual, household or institution. Behaviour at the system level is an emergent property of the collective behaviour at the local level, resulting from the interactions between agents and the environment through predisposed rules. Depending on the type of rules they abide by, ABM can be categorised as abstract, experimental, historical or empirical. The aim here was to determine whether results from participatory exercises with smallholder farmers could be utilised in the construction of an empirical ABM. By taking a systems approach, ABM can highlight knowledge gaps, where data and understanding of feedbacks may be lacking. However, a shortage of effective protocols and architectures to represent agents and their interactions may limit their potential. Behavioural decisions and coping strategies of farmers in the face of drought formed the focus of this study. A participatory rural appraisal (PRA) exercise was designed to elicit greater understanding of smallholder responses to drought; as well as the perceived impact of government interventions in the form of input subsidies. PRA is a broad term that encompasses a wide range of data collection techniques that aim to involve local stakeholders in analysing their own situation. Tools include matrix scoring, seasonal calendars and wellbeing ranking exercises. Results from participatory fieldwork were successfully incorporated into an agent-based model of Malawian smallholders. However, scarce availability of data was identified as a key factor undermining model robustness. Initial implementation of the model found inferences could be made concerning the impact of policy upon household decision-making and food security. Overall the study provides fertile ground for future work. It is hoped that by integrating PRA exercises and ABM it will be possible to create a collaborative framework that promotes interaction between scientists, policy makers and stakeholders, alike

Motivated by the increasing need for fast distributed processing of large-scale graphs such as the Web graph and various social networks, we study a number of fundamental graph problems in the message-passing model, where we have k machines that jointly perform computation on an arbitrary n-node (typically, n > k) input graph. The graph is assumed to be randomly partitioned among the k ≥ 2 machines (a common implementation in many real world systems). The communication is point-to-point, and the goal is to minimize the time complexity i.e., the number of communication rounds, of solving various fundamental graph problems.We present lower bounds that quantify the fundamental time limitations of distributively solving graph problems. We first show a lower bound of Ω(n/k) rounds for computing a spanning tree (ST) of the input graph. This result also implies the same bound for other fundamental problems such as computing a minimum spanning tree (MST), breadth-first tree (BFS), and shortest paths tree (SPT). We also show an Ω(n/k2) lower bound for connectivity ST verification and other related problems. Our lower bounds develop and use new bounds in random-partition communication complexity.To complement our lower bounds, we also give algorithms for various fundamental graph problems, e.g., PageRank, MST, connectivity, ST verification, shortest paths, cuts, spanners, covering problems, densest subgraph, subgraph isomorphism, finding triangles, etc. We show that problems such as PageRank, MST, connectivity, and graph covering can be solved in O(n/k) time (the notation O hides polylog(n) factors and an additive polylog(n) term); this shows that one can achieve almost linear (in k) speedup, whereas for shortest paths, we present algorithms that run in O(n/Ok) time (for (1 + e)-factor approximation) and in O(n/k) time (for O(log n)-factor approximation) respectively.Our results are a step towards understanding the complexity of distributively solving large-scale graph problems.

Milgram’s small-world experiments provided evidence for six degrees of separation, only a chain of five contacts separates any two random people. In theory, this small-world phenomenon is prevalent from a network structure perspective. However, empirical evidence shows that successful message chains are occasional, and the length of message chains are longer than the expected shortest path length. In this project, we construct a “null model” in order to examine how participants’ search strategies impact both the rate at which messages are successful routed and the length of these resulting paths. Using an agent-based modeling approach, we simulate different message routing situations based on the implementation of four different search strategies (i.e., random, memory, identity, and social search) on a network derived from students in a Northwestern University course entitled Collaborative Leadership and Decision Making. We find that the average completion rate for any of the search strategies differs in a statistically significant way from every other search strategy. The social search actually performs worse than if the message had traversed the network in a random walk. Additionally, results show a wide spread in the observed path length for each search strategy. Identity and social searches seemed the most effective for finding short paths, though it is possible that this is an artifact of the relative lack of messages completed overall. As a result, we conclude that simple search strategies may not be sufficient in explaining the empirical result, and that there is likely a more complex interaction taking place. Further, we see a disconnect between completion rate and observed path length, which suggests that many messages may fail to reach their targets as a result of network congestion.

Some graph analyses, such as social network and biological network, need large-scale graph construction and maintenance over distributed memory space. Distributed data-streaming tools, including MapReduce and Spark, restrict some computational freedom of incremental graph modification and run-time graph visualization. Instead, we take an agent-based approach. We construct a graph from a scientific dataset in CSV, tab, and XML formats; dispatch many reactive agents on it; and analyze the graph in the form of their collective group behavior: propagation, flocking, and collision. The key to success is how to automate the run-time construction and visualization of agent-navigable graphs mapped over distributed memory. We implemented this distributed graph-computing support in the multi-agent spatial simulation (MASS) library, coupled with the Cytoscape graph visualization software. This paper presents the MASS implementation techniques and demonstrates its execution performance in comparison to MapReduce and Spark, using two benchmark programs: (1) an incremental construction of a complete graph and (2) a KD tree construction.

Reconfiguring two shortest paths in a graph means modifying one shortest path to the other by changing one vertex at a time, so that all the intermediate paths are also shortest paths. This problem has several natural applications, namely: (a) revamping road networks, (b) rerouting data packets in a synchronous multiprocessing setting, (c) the shipping container stowage problem, and (d) the train marshalling problem. When modelled as graph problems, (a) is the most general case while (b), (c) and (d) are restrictions to different graph classes. We show that (a) is intractable, even for relaxed variants of the problem. For (b), (c) and (d), we present efficient algorithms to solve the respective problems. We also generalise the problem to when at most k (for some k >= 2) contiguous vertices on a shortest path can be changed at a time.

Reproducible modeling and simulation research has been identified as one of the Modeling and Simulation (M&S) Grand Challenge activities. Recently, uncertainty quantification has seen a renewed emphasis. While methods for verification and validation (V&V) have been widely developed for discrete-event simulations, newer simulation approaches such as the agent-based, agent-directed, and multi-agent simulation approaches introduce new V&V challenges. The active elements in these newer approaches have greater heterogeneity, e.g., every agent can be unique, with complex attributes and behaviors. Those behaviors can result in actions based on interaction with other agents, the environment, and even the outcome of simulated or artificial intelligence. The simulation spaces are often less constrained, e.g., rather than a network of servers and queues, the space can be continuous 2D Euclidian space with multiple associated geographic information systems (GIS) layers influencing the behavior of the actors. Over the last decade, a multitude of techniques has been used in agent-based modeling and simulation (ABMS) to perform V&V as well as replication and reproducibility (R&R) of the models. In this chapter, we present an overview of contributions in V&V, quality assurance (QA), and R&R of simulation studies, with special focus on ABMS. We also discuss the lessons learnt in V&V and replication from a series of simulation experiments using agent-based models (ABMs).

Borodin, Nielsen, and Rackoff ([5]) gave a model of greedy-like algorithms for scheduling problems and [1] extended their work to facility location and set cover problems. We generalize their notion to include other optimization problems, and apply the generalized framework to graph problems. Our goal is to define an abstract model that captures the intrinsic power and limitations of greedy algorithms for various graph optimization problems. We prove bounds on the approximation ratio achievable by such algorithms for basic graph problems such as shortest path, vertex cover, and others. Shortest path is an example of a problem where no algorithm in the FIXED priority model can achieve any approximation ratio (even one dependent on the graph size), but for which the well-known Dijkstra's algorithm shows that an ADAPTIVE priority algorithm can be optimal. We also prove that the approximation ratio for vertex cover achievable by ADAPTIVE priority algorithms is exactly 2. Here, a new lower bound matches the known upper bounds ([8]).

In the article, the definition of an undirected multiple graph of any natural multiplicity $$k > 1$$ is stated. There are edges of three types: ordinary edges, multiple edges, and multi-edges. Each edge of the last two types is the union of $$k$$ linked edges, which connect 2 or $$k + 1$$ vertices, correspondingly. The linked edges should be used simultaneously. If a vertex is incident to a multiple edge, it can be also incident to other multiple edges and it can be the common ending vertex to $$k$$ linked edges of some multi-edge. If a vertex is the common end of a multi-edge, it cannot be the common end of any other multi-edge. Also, a class of the divisible multiple graphs is considered. The main peculiarity of them is a possibility to divide the graph into $$k$$ parts, which are adjusted on the linked edges and which have no common ordinary edges. Each part is an ordinary graph. The following terms are generalized: the degree of a vertex, connectedness of a graph, the path, the cycle, the weight of an edge, and the path length. The definition of a reachability set for the ordinary and multiple edges is stated. The adjacency property is defined for a pair of reachability sets. It is shown, that we can check the connectedness of a multiple graph with the polynomial algorithm based on the search for reachability sets and testing their adjacency. A criterion of the existence of a multiple path between two given vertices is considered. The shortest multiple path problem is stated. Then, we suggest an algorithm for finding the shortest path in a multiple graph. It uses Dijkstra’s algorithm for finding the shortest paths in subgraphs, which correspond to different reachability sets.

Most economic and business systems are complex, dynamic, and nondeterministic systems. Different modeling techniques have been used for representing real life economic and business organizations either on a macro level (such as national economics) or micro level (such as business processes within a firm or strategies within an industry). Even though general computer simulation was used for modeling various systems (Zeigler, 1976) since the 1970s the limitation of computer resources did not allow for in-depth simulation of dynamic social phenomena. The dynamics of social systems and impact of the behavior of individual entities in social constructs were modeled using mathematical modeling or system dynamics. With the growing interest in multi agent systems that led to its standardization in the 1990s, multi agent systems were proposed for the use of modeling social systems (Gilbert & Conte, 1995). Multi agent simulation was able to provide a high level disintegration of the models and proper treatment of inhomogeneity and individualism of the agents, thus allowing for simulation of cooperation and competition. A number of simulation models were developed in the research of biological and ecological systems, such as models for testing the behavior and communication between social insects (bees and ants). Artificial systems for testing hypothesis about social order and norms, as well as ancient societies (Kohler, Gumerman, & Reynolds, 2005) were also simulated. Since then, agent-based modeling and simulation (ABMS) established itself as an attractive modeling technique (Klugl, 2001; Moss & Davidsson, 2001). Numerous software toolkits were released, such as Swarm, Repast, MASON and SeSAm. These toolkits make agent-based modeling easy enough to be attractive to practitioners from a variety of subject areas dealing with social interactions. They make agent-based modeling accessible to a large number of analysts with less programming experience.

Agent-Based Modeling can contribute to the understanding of international trade processes. Models for the effects of culture and cultural differences on agent behavior are required for realistic agent-based simulation of international trade. This paper makes a step toward modeling of culture in agents. The focus is one of the five dimensions of culture according to Hofstede: individualism versus collectivism. The paper presents an analysis based on social science literature about national cultures. For cultural differentiation of agent behavior, rules are formulated for individualist versus collectivist agent behavior with respect to negotiations, cooperation or defection in the delivery phase of transactions, trade partner selection, and trust. Example computations demonstrate the feasibility in multi-agent simulations.

This article discusses approaches to computer modeling of complex systems in various subject areas. The modeling of such systems should be implemented using the technology of agents. Each agent has an individual behavior and can cooperate with other agents to perform certain functions of the system. As a tool of agent-based modelling is considered the NetLogo program distributed under the GNU GPL. Provides information about the NetLogo programming language, discusses the paradigm of agent-based modelling in virtual World NetLogo. Provides information about agent simulation of vehicle movement, discusses the interaction between agents on the principle of predator-prey, provides information about landscape models provide information for modeling the movement of agents in the network. Agent-based model created in Netlogo, can be equipped with an interface for data input and output parameters of the simulated system.

We present the buffer heap , a cache-oblivious priority queue that supports Delete-Min , Delete , and a hybrid Insert / Decrease-Key operation in O (1/ B log 2 N / M ) amortized block transfers from main memory, where M and B are the (unknown) cache size and block size, respectively, and N is the number of elements in the queue. We introduce the notion of a slim data structure that captures the situation when only a limited portion of the cache, which we call a slim cache , is available to the data structure to retain data between data structural operations. We show that a buffer heap automatically adapts to such an environment and supports all operations in O (1/λ + 1/ B log 2 N /λ) amortized block transfers each when the size of the slim cache is λ. Our results provide substantial improvements over known trivial cache performance bounds for cache-oblivious priority queues with Decrease-Keys . Using the buffer heap, we present cache-oblivious implementations of Dijkstra’s algorithm for undirected and directed single-source shortest path (SSSP) problems for graphs with non-negative real edge-weights. On a graph with n vertices and m edges, our algorithm for the undirected case performs O ( n + m / B log 2 n / M ) block transfers and for the directed case performs O (( n + m / B ) ċ log 2 n / B ) block transfers. These results give the first non-trivial cache-oblivious bounds for the SSSP problem on general graphs. For the all-pairs shortest path (APSP) problem on weighted undirected graphs, we incorporate slim buffer heaps into multi-buffer-buffer-heaps and use these to improve the cache-aware cache complexity. We also present a simple cache-oblivious APSP algorithm for unweighted undirected graphs that performs O ( mn / B log M / B n / B ) block transfers. This matches the cache-aware bound and is a substantial improvement over the previous cache-oblivious bound for the problem.