Stochastic interactions increase cooperation in a spatial Prisoner's Dilemma

This article implements the spatial Prisoner's Dilemma (PD) as an agent‐based model. Many previous models have assumed that agents in a spatial PD interaction exclusively and deterministically within their von Neumann neighborhood. The model presented here introduces stochastic interactions within a subset of the von Neumann neighborhood. This implementation allows a direct comparison of the effect of stochastic interactions relative to deterministic interactions on the level of cooperation that emerges in the system. The results show that when holding the total number of interactions agents participate in each round constant, allowing agents to interact stochastically increases cooperation in the system relative to deterministic interactions. © 2014 Wiley Periodicals, Inc. Complexity 21: 82–92, 2015     

Cooperation in bike racing—When to work together and when to go it alone

We present an agent‐based model of bicycle racing that incorporates both physiology and the types of multiplayer scenarios that arise in real races. In these scenarios, riders can choose to share the workload with other riders (cooperate) or pursue their own self‐interests (defect). We compare the model's predictions to race situations and use it to investigate how different strategies can affect outcomes. We find that an individual player's best strategy depends on fitness level: below‐average riders fare better as defectors whereas above‐average riders perform better as cooperators. The strategies of stronger riders affect their teammates' results as well. The teammates of defecting strong riders fare worse overall than the teammates of cooperating strong riders. These results reproduce a dynamic that played out in the 2009 Tour de France. The winner, Alberto Contador, pursued a strategy perceived by many to be unusually uncooperative by repeatedly defecting on his teammates. The strategy worked to his advantage but may have negatively affected his teammates' placements. © 2011 Wiley Periodicals, Inc. Complexity, 17,39–44, 2011     

Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map

Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a matrix of link strengths, thus generalizing the technique of &201C;collaborative filtering&201D;; 2) learning web rules extract information from a user&2018;s sequential path through the web in order to change link strengths and create new links. The resulting weighted web can be used to facilitate problem-solving by suggesting related links to the user, or, more powerfully, by supporting a software agent that discovers relevant documents through spreading activation.     

聚集数据下回归系数混合估计的相对效率

讨论在聚集数据情形下,具有附加信息的线性回归模型的参数估计,提出了回归系数的聚集混合估计,研究了该估计相对于Peter—Karsten估计和相对于最小二乘估计的相对效率,得到了相对效率的上、下界.     

The effects of mental model formation on group decision making: An agent‐based simulation

We investigated dynamics of group decision making on complex problems when agents can form mental models of others from discussion history. Results indicated that as the agents' memory capacity increases, the group reaches superficial consensus more easily. Surprisingly, however, the shared mental model of the problem develops only within a limited area of the problem space, because incorporating knowledge from others into one's own knowledge quickly creates local agreement on where relevant solutions are, leaving other potentially useful solutions beyond the scope of discussion. The mechanisms stifling group‐level exploration and their implications for decision making research are discussed. © 2010 Wiley Periodicals, Inc. Complexity 16: 49–57, 2011     

Agent‐based model for economic impact of free software

This article describes the potential impact that free (i.e., open source) software can have on an existing commercial software market. A model for the software market is constructed in terms of autonomous agents, which represent the users, the companies, and the free software providers. The model specifies a reservation price for each user agent and develops a gradient learning strategy for revenue‐maximizing company agents. Simulations explore parameters such as the demand distribution, and the relative importance of market share, advertising and random effects in product visibility. Results from the case without free software show a prevalence of monopolies, which is consistent with other studies of high‐technology market economics. The effects of free software are not uniform, but are highly parameter dependent. A “capture region” is found in which free software eventually dominates the market. © 2003 Wiley Periodicals, Inc.     

Cultural differences and collective action: A social network perspective

This study investigates how cultural differences on the individualism–collectivism (I–C) dimension of social networks influence the outcomes of collective action. Evidence shows that I–C values are indicators of how people construct their social networks and use strong/weak ties as a behavioral reference. Specifically, when compared with individualists, collectivists tend to hold larger strong‐tie networks and endow strong ties with greater interpersonal influence. Results obtained from agent‐based modeling indicate that individualistic cultures are more effective at propagating collective action when one of the two following conditions is met: (1) people have a strong motivation to participate and (2) the connectivity of the social system is low. In contrast, spread of collective action in collectivistic cultures is more effective when motivation is not strong and the connectivity of the social system is high. These findings call for a serious consideration of the role of culture in collective action. © 2014 Wiley Periodicals, Inc. Complexity 20: 68–77, 2015     

主观贴现率差异对集体物品产出的影响

通过引入集体物品的初始存量和衰竭速率两个参数,建立跨期均衡模型,考察个体主观贴现率差异程度对集体行动合作产出的影响,讨论参数条件发生变化时的贴现率异质性效应.结论表明:个体对未来收益(损失)越重视(即主观贴现率越大),集体物品的合作生产越容易维系;且初始存量和衰竭速率构成了集体物品自发供给的必要条件.     

Challenge Versus Exchange in Collective Decision Making: A Comparison of Two Simulation Models Based on Simulated Data

In collective decision making, actors can use different influence strategies to get their way. Differences in influence strategies may, or may not, be connected to differences in collective outcomes. This research studies two influence strategies: the exchange strategy and the challenge strategy. In the existing literature, these strategies are analyzed and compared using simulation models in which actor behavior regarding influence attempts based on one of the strategies is modeled explicitly. Until now, these models have been tested only empirically on limited data sets. However, a theoretical test is necessary to gain more precise insights in the effect of characteristics of collective decision making situations on the collective outcomes. In the present research, computer simulations are used in a structured comparison of two competing models (the iterative exchange model and challenge model). The analyses show that the outcomes of both models are captured for a large part in the actor characteristics on the issues. Besides this, the expected directions of challenges and exchanges play a major part in explaining the outcomes of the models. This research shows that the use of simulated data allows a structured search of the input space, which led to new insights into the iterative exchange model and challenge model, and therefore in the exchange strategy and the challenge strategy.     

Co‐existence of Poisson and non‐Poisson processes in ordered parallel multilane pedestrian traffic

We show that non‐Poisson and Poisson processes can coexist in ordered parallel multilane pedestrian traffic, in the presence of lane switching which asymmetrically benefits the switchers and nonswitchers. Pedestrians join at the tail end of a queue and transact at the opposite front end. Their aim is to complete a transaction within the shortest possible time, and they can transfer to a shorter queue with probability p_s. Traffic is described by the utilization parameter U = λ〈t_s〉/N, where λ is the average rate of pedestrians entering the system, 〈t_s〉 is the average transaction time, and N is the number of lanes. Using an agent‐based model, we investigate the dependence of the average completion time 〈t_c〉 with variable K = 1 + (1 ? U)^?1 for different N and 〈t_s〉 values. In the absence of switching (p_s = 0), we found that 〈t_c〉 ∝ K^τ, where τ ≈ 1 regardless of N and 〈t_s〉. Lane switching (p_s = 1) reduces 〈t_c〉 for a given K, but its characteristic dependence with K differs for nonswitchers and switchers in the same traffic system. For the nonswitchers, 〈t_c〉 ∝ K^τ, where τ < 1. At low K values, switchers have a larger 〈t_c〉 that also increases more rapidly with K. At large K, the increase rates become equal for both. For nonswitchers, the possible t_c values obey an exponentially decaying probability density function p(t_c). The switchers on the other hand, are described by a fat‐tailed p(t_c) implying that a few are penalized with t_c values that are considerably longer than any of those experienced by nonswitchers. © 2006 Wiley Periodicals, Inc. Complexity 11: 35–42, 2006     

Emergence and induction of cellular automata rules via probabilistic reinforcement paradigms

Two simple models of emergence and automated induction are described. In the first, an initially random process comes, over time, to emulate a deterministic process with noise. In the second, an induction algorithm is used to make unbiased best guess estimates of cellular automata rules generating a given time series of binary strings. The general conclusions are as follows: (1) that it may not be possible to distinguish between a stochastic process with selection and reinforcement and a noisy deterministic process; and (2) automated induction algorithms will often be vulnerable to errors of type 1 when faced with random data. In this second case, this leads to a method for study of the modeling class assumed in the induction algorithm. © 2006 Wiley Periodicals, Inc. Complexity 11: 44–57, 2006     

Agent‐based simulation of an N‐person game with parabolic payoff functions

We report computer simulation experiments based on our agent‐based simulation tool to model a new N‐person game based on John Conway's Game of Life. The individual agents may choose between two behavior options: cooperation or defection. The payoff (reward/penalty) functions are given as two parabolas: one for each option. After a certain number of iterations, the behavior of the agents stabilizes to either a constant value or oscillates around such a value. The simulation's goal is to investigate the effects of intermediate behavior on a society of agents. We have performed a systematic investigation of this game for all six possible cases of the mutual positions of parabolic payoff functions crossing each other at two points: x = 0.3 and 0.7 where x is the ratio of the cooperation choice to the total number of agents in the agent's neighborhood. The global ratios X(t) of the total number of cooperators in the entire array of agents as functions of time (iterations) and the solutions of the game X_final as functions of X₀ were observed for each case for Pavlovian, greedy, and conformist agents. The solutions have predictable tendencies only when the neighborhood is the entire array of greedy or conformist agents. In all other cases unexpected properties emerge. © 2009 Wiley Periodicals, Inc. Complexity, 2010     

(CMMSE paper) A finite‐difference model for indoctrination dynamics

In this work, a system of non‐linear difference equations is employed to model the opinion dynamics between a small group of agents (the target group) and a very persuasive agent (the indoctrinator). Two scenarios are investigated: the indoctrination of a homogeneous target group, in which each agent grants the same weight to his (or her) partner's opinion and the indoctrination of a heterogenous target group, in which each agent may grant a different weight to his or her partner's opinion. Simulations are performed to study the required times by the indoctrinator to convince a group. Initially, these groups are in a consensus about a doctrine different to that of the ideologist. The interactions between the agents are pairwise.     

A systematic analysis of the N‐person chicken game

We report computer simulation experiments based on our agent‐based simulation tool to model the multiperson Chicken dilemma game for the case when the agents are greedy simpletons who imitate the action of that of their neighbors who received the highest payoff for its previous action. The individual agents may cooperate with each other for the collective interest or may defect, i.e., pursue their selfish interests only. After a certain number of iterations the proportion of cooperators stabilizes to either a constant value or oscillates around such a value. The payoff (reward/penalty) functions are given as two straight lines: one for the cooperators and another for the defectors. The payoff curves are functions of the ratio of cooperators to the total number of agents. Even for linear payoff functions, we have four free parameters that determine the payoff functions that have the following properties: (1) Both payoff functions increase with the increasing number of cooperators. (2) In the region of low cooperation the cooperators have a higher reward than the defectors. (3) When the cooperation rate is high, there is a higher payoff for defecting behavior than for cooperating behavior. (4) As a consequence, the slope of the D function is greater than that of the C function and the two payoff functions intersect. (5) All agents receive a lower payoff if all defect than if all cooperate. We have investigated the behavior of the agents systematically. The results show that the solutions have predictable tendencies but they are nontrivial and quite irregular. The solutions show drastic changes in the parameter ranges 0.6 ≤ R ≤ 0.65 for all values of S and 0 ≤ S ≤ 0.2 when R < 0.6 (R is the reward for mutual cooperation and S is the sucker's payoff to a lonely cooperator). © 2010 Wiley Periodicals, Inc. Complexity, 2010     

国有及集体企业对我国养老保险制度影响的实证研究

本文从定量的角度分析了国有企业和集体企业对我国的养老保险制度影响,说明了集体企业在缓解当前养老保险制度所面临的压力方面起到至关重要的作用.     

Evolutionary dynamics of knowledge

This study discusses the evolutionary nature of knowledge acquisition at micro and macro levels, and in particular when the process involves an artificial agent's interpretative devices. In order to accomplish this, we propose using an individual learning model (or inner‐world reconstruction model) that in our view overcomes neoclassic epistemological holdups and may increase the predictive power of computational economics, by letting an artificial agent's knowledge evolve by itself, irrespective of globally specified goals or individual motives of behavior; using simultaneous (or parallel) genetic algorithms (GA) to evolve a single agent's learning strategy, each GA with different general specifications, in a multiagent setting. © 2006 Wiley Periodicals, Inc. Complexity 11: 12–19, 2006     

SPATIAL EFFECTS IN AN INDIVIDUAL‐BASED MODEL OF PRODUCER–HERBIVORE INTERACTION

Abstract In this paper, we describe an individual‐based model of the interaction between a producer and herbivore species. The interaction occurs in a 2‐dimensional matrix of individual cells. The producer organism grows in the cells and the herbivores move between the cells, towards areas of high concentration of producer. Herbivores may die of starvation or they may reproduce asexually. The model is not built to represent a specific existing system but is a parsimonious generalized model of producer–herbivore interaction designed to test spatial effects. Although the model algorithm is kept simple—the herbivores have no learning or social behavior and have a minimal foraging strategy—the model displays some characteristics of real systems such as metapopulation behavior. We find significant effects on the herbivore and producer populations of the maximum move distance per step of the herbivores, the shape of the two‐dimensional matrix, the quality of a corridor between patches habitable by the herbivores, and edge effects with fuzzy edges. The behavior of the model is comparable with empirical observations found in the literature, and we suggest that this simple model may be used to investigate the mechanism of these real effects.     

On rates of convergence of information theoretic criterion in rank determination of one-way random effects models

In this paper, upper bounds on the probabilities of wrong determination of the rank of covariance matrix of random effects in one-way random effects models are given, based on the information theoretic criterion. Under weak conditions, the bounds are shown of exponential-type.The work was partially supported by Natural Sciences and Engineering Research Council of Canada and Faculty Research Grant, Faculty of Arts, York University.     

Analytical solutions of N‐person games

The possibility of analytical solutions of N‐person games is presented. A simple formula provides valuable information about the outcomes of such games with linear payoff functions and Pavlovian agents. Experiments performed with our simulation tool for the multiagent stag hunt dilemma game are presented. For the case of Pavlovian agents the game has nontrivial but remarkably regular solutions. If both payoff functions are linear and the real solutions of Eq. (2) are both positive, then the analytical solutions are remarkably accurate. © 2012 Wiley Periodicals, Inc. Complexity, 2012