The proliferation of agent-based models (ABMs) in recent decades has motivated model practitioners to improve the transparency, replicability, and trust in results derived from ABMs. The complexity of ABMs has risen in stride with advances in computing power and resources, resulting in larger models with complex interactions and learning and whose outputs are often high-dimensional and require sophisticated analytical approaches. Similarly, the increasing use of data and dynamics in ABMs has further enhanced the complexity of their outputs. In this article, we offer an overview of the state-of-the-art approaches in analysing and reporting ABM outputs highlighting challenges and outstanding issues. In particular, we examine issues surrounding variance stability (in connection with determination of appropriate number of runs and hypothesis testing), sensitivity analysis, spatio-temporal analysis, visualization, and effective communication of all these to non-technical audiences, such as various stakeholders.

Abstract. While agent-based models (ABMs) are becoming increasingly popular for simulating complex and emergent phenomena in many fields, understanding and analyzing ABMs poses considerable challenges. ABM behavior often depends on many model parameters, and the task of exploring a model's parameter space and discovering the impact of different parameter settings can be difficult and time-consuming. Exhaustively running the model with all combinations of parameter settings is generally infeasible, but judging behavior by varying one parameter at a time risks overlooking complex nonlinear interactions between parameters. Alternatively, we present a case study in computer-aided model exploration, demonstrating how evolutionary search algorithms can be used to probe for several qualitative behaviors (convergence, non-convergence, volatility, and the formation of vee shapes) in two different flocking models. We also introduce a new software tool (BehaviorSearch) for performing parameter search on ABMs created in the NetLogo modeling environment.Key words: parameter search, model exploration, genetic algorithms, flocking, agent-based modeling, ABM, multi-agent simulation MotivationAgent-based modeling is a powerful simulation technique in which many agents interact according to simple rules resulting in the emergence of complex aggregatelevel behavior. This technique is becoming increasingly popular in a wide range of scientific endeavors, due to the power it has to simulate many different natural and artificial processes [1][2][3]. A crucial step in the modeling process is an analysis of how the system's behavior is affected by the various model parameters. However, the number of controlling parameters and range of parameter values in an agent-based model (ABM) is often large, the computation required to run a model is often significant, and agent-based models are typically stochastic in nature, meaning that multiple trials must be performed to assess the model's behavior. These factors combine to make a full brute-force exploration of the parameter space infeasible. Researchers respond to this difficulty in a variety of ways. One common approach is to run factorial-design experiments that either explore model behavior only in a small subspace or explore the full space

This paper provides the first comprehensive survey of methods for inserting arbitrary data into Bitcoin’s blockchain. Historical methods of data insertion are described, along with lesser-known techniques that are optimized for efficiency. Insertion methods are compared on the basis of efficiency, cost, convenience of data reconstruction, permanence, and potentially negative impact on the Bitcoin ecosystem.

One method of viral marketing involves seeding certain consumers within a population to encourage faster adoption of the product throughout the entire population. However, determining how many and which consumers within a particular social network should be seeded to maximize adoption is challenging. We define a strategy space for consumer seeding by weighting a combination of network characteristics such as average path length, clustering coefficient, and degree. We measure strategy effectiveness by simulating adoption on a Bass-like agent-based model, with five different social network structures: four classic theoretical models (random, lattice, small-world, and preferential attachment) and one empirical (extracted from Twitter friendship data). To discover good seeding strategies, we have developed a new tool, called BehaviorSearch, which uses genetic algorithms to search through the parameter-space of agent-based models. This evolutionary search also provides insight into the interaction between strategies and network structure. Our results show that one simple strategy (ranking by node degree) is near-optimal for the four theoretical networks, but that a more nuanced strategy performs significantly better on the empirical Twitter-based network. We also find a correlation between the optimal seeding budget for a network, and the inequality of the degree distribution. MOTIVATIONViral marketing, or word-of-mouth marketing, is based on the idea that consumer discussions about a product are Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. GECCO'10, July 7-11, 2010, Portland, Oregon, USA. Copyright 2010 ACM 978-1-4503-0072-8/10/07 ...$10.00. more powerful than traditional advertising. One way to encourage positive word-of-mouth is by distributing reduced or free products to target consumers who will then discuss the product with their friends and encourage those friends to buy the product. However, whom to seed with these initial products in order to maximize the amount and rate of product adoption is not obvious. Given an arbitrary social network and a limited seeding budget, choosing the optimal seeding locations has been shown to be an NP-Hard problem [16]. Furthermore, it is not clear what the proper seeding budget should be for a particular network. Assuming that the product is beneficial and that seeded consumers are inclined to speak positively about it, seeding more consumers will increase the speed of product adoption. However, giving away more free products increases the overall expense of the promotional campaign. In addition, seeded consumers are removed from the pool of potential customers, which may decrease total revenue for the prod...

Variations in permeability have been found to significantly affect the flow of water though hyporheic systems, especially in regions with discontinuous transitions between distinct streambed lithologies. In this study, we probabilistically arranged two sediments (sand and sandy gravel) in a grid framework and imposed a single hyporheic flow cell across the grid to investigate how discontinuous permeability fields influence volumetric flow and residence time distributions. We used both a physical system and computer simulations to model flow through this sediment grid. A solution of blue dye and salt was pumped into the system and used to detect flow. We recorded the dye location using time-lapse photography and measured the electrolytic conductivity levels as the water exited the system as a proxy for salt concentration. We also used a computer simulation to calculate dye-fronts, residence times, and exiting salt concentrations for the modeled system. Comparison between simulations and physical measurements yielded strong agreement. In further simulations with 300 different grids, we found a strong correlation between volumetric flow rate and the placement of high permeability grid cells in regions of high hydraulic head gradients. One implication is that small anomalies in streambed permeability have a disproportionately large influence on hyporheic flows when located near steep head gradients such as steps. We also used moving averages with varying window sizes to investigate the effect of the abruptness of transitions between sediment types. We found that smoother permeability fields increased the volumetric flow rate and decreased the median residence times.

Linguistic norms emerge in human communities because people imitate each other. A shared linguistic system provides people with the benefits of shared knowledge and coordinated planning. Once norms are in place, why would they ever change? This question, echoing broad questions in the theory of social dynamics, has particular force in relation to language. By definition, an innovator is in the minority when the innovation first occurs. In some areas of social dynamics, important minorities can strongly influence the majority through their power, fame, or use of broadcast media. But most linguistic changes are grassroots developments that originate with ordinary people. Here, we develop a novel model of communicative behavior in communities, and identify a mechanism for arbitrary innovations by ordinary people to have a good chance of being widely adopted.To imitate each other, people must form a mental representation of what other people do. Each time they speak, they must also decide which form to produce themselves. We introduce a new decision function that enables us to smoothly explore the space between two types of behavior: probability matching (matching the probabilities of incoming experience) and regularization (producing some forms disproportionately often). Using Monte Carlo methods, we explore the interactions amongst the degree of regularization, the distribution of biases in a network, and the network position of the innovator. We identify two regimes for the widespread adoption of arbritrary innovations, viewed as informational cascades in the network. With moderate regularization of experienced input, average people (not well-connected people) are the most likely source of successful innovations. Our results shed light on a major outstanding puzzle in the theory of language change. The framework also holds promise for understanding the dynamics of other social norms.

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