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20 articles matched your search for the keywords:
Education, Inequality, Aspiration, Schools, School-Place Allocation, Parental Choice

Simulating Norms, Social Inequality, and Functional Change in Artificial Societies

Nicole J. Saam and Andreas G. Harrer
Journal of Artificial Societies and Social Simulation 2 (1) 2

Kyeywords: Simulation of Norms, Social Inequality, Functions of Norms
Abstract: In this paper, we compare the computational and sociological study of norms, and resimulate previous simulations (Conte and Castelfranchi 1995a, Castelfranchi, Conte and Paolucci 1998) under slightly different conditions. First, we analyze the relation between norms, social inequality and functional change more closely. Due to our results, the hypothesis stating that the "finder-keeper" norm while controlling aggression efficaciously reduces social inequality holds only in quite egalitarian societies. Throughout a variety of inegalitarian societies, it instead increases social inequality. This argument which can be traced back to Marx is being investigated by use of computer simulations of artificial societies. Second, we remodel normative behaviour from a sociological point of view by implementing Haferkamp's theory of action approach to deviant behaviour. Following the game theoretic models, the computational study of norms has up to now ignored the importance of power in explaining how norms affect social behaviour, how norms emerge, become established and internalized, and change. By simulating Haferkamp and repeating the Conte and Castelfranchi experiments, we demonstrate that it is possible to integrate power into computational models of norms.

Small World Dynamics and The Process of Knowledge Diffusion: The Case of The Metropolitan Area of Greater Santiago De Chile

Piergiuseppe Morone and Richard Taylor
Journal of Artificial Societies and Social Simulation 7 (2) 5

Kyeywords: Agent-based, Chile, Inequality, Knowledge, Network, Small world
Abstract: This paper aims to understand some of the mechanisms which dominate the phenomenon of knowledge diffusion in the process that is called ‘interactive learning’. We examine how knowledge spreads in a network in which agents have ‘face-to-face’ learning interactions. We define a social network structured as a graph consisting of agents (vertices) and connections (edges) and situated on a grid which resembles the geographical characteristics of the metropolitan area of Greater Santiago de Chile. The target of this simulation is to test whether knowledge diffuses homogeneously or whether it follows some biased path generating geographical divergence between a core area and a periphery. We also investigate the efficiency of our ‘preference’ model of agent decision-making and show that this system evolves towards a small-world type network.

Case-Based Reasoning, Social Dilemmas, and a New Equilibrium Concept

Luis R. Izquierdo, Nicholas M. Gotts and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 7 (3) 1

Kyeywords: Social Dilemmas, Case-Based Reasoning, Prisoner's Dilemma, Agent-Based Simulation, Analogy, Game Theory, Aspiration Thresholds, Equilibrium
Abstract: In this paper social dilemmas are modelled as n-player games. Orthodox game theorists have been able to provide several concepts that narrow the set of expected outcomes in these models. However, in their search for a reduced set of solutions, they had to pay a very high price: they had to make disturbing assumptions such as instrumental rationality or common knowledge of rationality, which are rarely observed in any real-world situation. We propose a complementary approach, assuming that people adapt their behaviour according to their experience and look for outcomes that have proved to be satisfactory in the past. These ideas are investigated by conducting several experiments with an agent-based simulation model in which agents use a simple form of case-based reasoning. It is shown that cooperation can emerge from the interaction of selfish case-based reasoners. In determining how often cooperation occurs, aspiration thresholds, the agents' representation of the world, and their memory all play an important and interdependent role. It is also argued that case-based reasoners with high enough aspiration thresholds are not systemically exploitable, and that if agents were sophisticated enough to infer that other players are not exploitable either, they would eventually cooperate.

Increasing Learner Retention in a Simulated Learning Network Using Indirect Social Interaction

Rob E.J.R. Koper
Journal of Artificial Societies and Social Simulation 8 (2) 5

Kyeywords: Self-Organisation, Education, Distance Learning, Lifelong Learning, Learning Network
Abstract: A learning network is a network of persons who create, share, support and study units of learning (courses, workshops, lessons, etc.) in a specific knowledge domain. Such networks may consist of a large number of alternative units of learning. One of the problems learners face in a learning network is to select the most suitable path through the units of learning in order to build the required competence in an effective and efficient way. This study explored the use of indirect social interaction to solve this problem. Units of learning that have been completed successfully by other comparable learners are presented to the learners as navigational support. A learning network is simulated in which learners search for, enrol in and study units of learning, subject to a variety of constraints: a) variable quality of the different units of learning, b) disturbance, i.e. interference by priorities other than learning and c) matching errors that occur when the entry requirements of the selected unit of learning do not align with the pre-knowledge of the learner. Two conditions are explored in the network: the selection of units of learning with and without indirect social interaction. It was found that indirect social interaction increases the proportion of learners who attain their required competence in the simulated learning network.

Peer-Allocated Instant Response (PAIR): Computational Allocation of Peer Tutors in Learning Communities

Wim Westera
Journal of Artificial Societies and Social Simulation 10 (2) 5

Kyeywords: Distance Learning, Computational Simulations, System Dynamics, Education and Application, Peer Support, Peer Allocation
Abstract: This paper proposes a computational model for the allocation of fleeting peer tutors in a community of learners: a student\'s call for support is evaluated by the model in order to allocate the most appropriate peer tutor. Various authors have suggested peer tutoring as a favourable approach for confining the ever-growing workloads of teachers and tutors in online learning environments. The model\'s starting point is to serve two conflicting requirements: 1) the allocated peers should have sufficient knowledge to guarantee high quality support and 2) tutoring workload of peers should be fairly distributed over the student population. While the first criterion is likely to saddle a small number of very bright students with all the tutoring workload, the unconditional pursuit of a uniform workload distribution over the students is likely to allocate incompetent tutors. In both cases the peer support mechanism is doomed to failure. The paper identifies relevant variables and elaborates an allocation procedure that combines various filter types. The functioning of the allocation procedure is tested through a computer simulation program that has been developed to represent the student population, the students curriculum and the dynamics of tutor allocation. The current study demonstrates the feasibility of the self-allocating peer tutoring mechanism. The proposed model is sufficiently stable within a wide range of conditions. By introducing an overload tolerance parameter which stretches the fair workload distribution criteria, substantial improvements of the allocation success rate are effected. It is demonstrated that the allocation algorithm works best at large population sizes. The results show that the type of curriculum (collective route or individualised routes) has only little influence on the allocation mechanism.

Towards a Community Framework for Agent-Based Modelling

Marco A. Janssen, Lilian N. Alessa, C. Michael Barton, Sean Bergin and Allen Lee
Journal of Artificial Societies and Social Simulation 11 (2) 6

Kyeywords: Replication, Documentation Protocol, Software Development, Standardization, Test Beds, Education, Primitives
Abstract: Agent-based modelling has become an increasingly important tool for scholars studying social and social-ecological systems, but there are no community standards on describing, implementing, testing and teaching these tools. This paper reports on the establishment of the Open Agent-Based Modelling Consortium, www.openabm.org, a community effort to foster the agent-based modelling development, communication, and dissemination for research, practice and education.

When and How to Imitate Your Neighbours: Lessons from and for FEARLUS

Nicholas M. Gotts and J. Gareth Polhill
Journal of Artificial Societies and Social Simulation 12 (3) 2

Kyeywords: Imitation, Innovation, Aspiration, Land-Use, Spatio-Temporal Heterogeneity
Abstract: This paper summarises some previously published work on imitation, experimentation (or innovation) and aspiration thresholds using the FEARLUS modelling system and reports new work with FEARLUS extending these studies. Results are discussed in the context of existing literature on imitation and innovation in related contexts. A form of imitation in which land uses are selected on the criterion of their recent performance within the neighbourhood of the land parcel concerned (called here 'Best-mean Imitation'), outperforms comparably simple forms of imitation in a wide range of FEARLUS Environments. However, the choice of criterion is shown to interact with both the way the criterion is applied, and the land manager's aspiration threshold: the level of return with which they are satisfied. The implications of work with FEARLUS for the broader bodies of research discussed, and vice versa, are considered.

Income Distribution Effects of a Finnish Work Incentive Trap Reform

Paivi Mattila-Wiro
Journal of Artificial Societies and Social Simulation 12 (3) 3

Kyeywords: Work Incentive Trap Reforms, Microsimulation, Disposable Income, Economic Well-Being, Inequality, Poverty
Abstract: The present study concentrates on the income distribution effects of A Finnish Work Incentive Trap Reform started in 1996. I estimate how the reforms made have affected income levels and income inequality - the distribution of economic wellbeing. I look at the effects both without and with behavioral response. The data used is the Income Distribution Statistics of Statistics Finland from the years 1996 and 1998. The empirical part of the study is based on a microsimulation model. The method of microsimulation is a powerful tool for the analysis of ex post evaluation of policy reforms. However, the method is rarely and on very few occasions applied in Finland. The results drawn without behavioral response show that the 1996 data with the 1998 legislation produces lower values for income inequality measures and higher average income levels for almost all income decile groups compared to those with the 1996 legislation. However, the changes are very small. When the labor supply effect is included, the lowest incomes rise only very little (in fact, hardly at all) and the Gini coefficient remains unaltered.

Using Microsimulation to Optimize an Income Transfer System Towards Poverty Reduction

Seppo Sallila
Journal of Artificial Societies and Social Simulation 13 (1) 1

Kyeywords: Inequality, Optimization, Poverty, Public Policy, Simulation Methodology, Tax-Benefit System
Abstract: In this study, a static microsimulation model SOMA is used to optimize Finland's tax-benefit legislation to alleviate poverty or at least to reduce it significantly. The method is a classical optimization method using a greed optimization strategy. This means an iterative process, where only one poverty diminishing parameter is changed by 10% from its earlier value at each iteration. Expenses are also optimized to reduce inequality as measured by the Gini-coefficient. Revenues and expenses are balanced at every iteration. Certain parameters of social assistance were found to be the most effective in reducing poverty. However by raising substantially the basic unemployment benefit, basic pensions, housing benefits and study grants - leaving social assistance untouched - poverty was reduced by under 50 percent. This means that social assistance is still required to reduce poverty further. Costs are most effectively financed by raising capital income tax.

Large Scale Daily Contacts and Mobility Model - an Individual-Based Countrywide Simulation Study for Poland

Franciszek Rakowski, Magdalena Gruziel, Michal Krych and Jan P Radomski
Journal of Artificial Societies and Social Simulation 13 (1) 13

Kyeywords: Agent Based Model, Educational Availability, Daily Commuting, Social Network, Virtual Society Simulations
Abstract: In this study we describe a simulation platform used to create a virtual society of Poland, with a particular emphasis on contact patterns arising from daily commuting to schools or workplaces. In order to reproduce the map of contacts, we are using a geo-referenced Agent Based Model. Within this framework, we propose a set of different stochastic algorithms, utilizing available aggregated census data. Based on this model system, we present selected statistical analysis, such as the accessibility of schools or the location of rescue service units. This platform will serve as a base for further large scale epidemiological and transportation simulation studies. However, the first approach to a simple, country-wide transportation model is also presented here. The application scope of the platform extends beyond the simulations of epidemic or transportation, and pertains to any situation where there are no easily available means, other than computer simulations, to conduct large scale investigations of complex population dynamics.

Role-Playing Game and Learning for Young People About Sustainable Development Stakes: An Experiment in Transferring and Adapting Interdisciplinary Scientific Knowledge

Françoise Gourmelon, Mathias Rouan, Jean-François Lefevre and Anne Rognant
Journal of Artificial Societies and Social Simulation 14 (4) 21

Kyeywords: Children Education, Multi-Agent Environment, Role-Playing Game
Abstract: The study refers to the interactions between socio-economic and natural dynamics in an island biosphere reserve by using companion modelling. This approach provides scientific results and involves interdisciplinarity. In the second phase of the study, we transferred knowledge by adapting the main research output, a role-playing game, to young people. Our goal was to introduce interactions between social and ecological systems, coastal dynamics and integrated management. Adapting the game required close collaboration between the scientists and educators in order to transform both its substance and form and to run it with an easy-to-handle ergonomic platform.

Aspiration, Attainment and Success: An Agent-Based Model of Distance-Based School Allocation

James Millington, Tim Butler and Chris Hamnett
Journal of Artificial Societies and Social Simulation 17 (1) 10

Kyeywords: Education, Inequality, Aspiration, Schools, School-Place Allocation, Parental Choice
Abstract: In recent years, UK governments have implemented policies that emphasise the ability of parents to choose which school they wish their child to attend. Inherently spatial school-place allocation rules in many areas have produced a geography of inequality between parents that succeed and fail to get their child into preferred schools based upon where they live. We present an agent-based simulation model developed to investigate the implications of distance-based school-place allocation policies. We show how a simple, abstract model can generate patterns of school popularity, performance and spatial distribution of pupils which are similar to those observed in local education authorities in London, UK. The model represents ‘school’ and ‘parent’ agents. Parental ‘aspiration’ to send their child to the best performing school (as opposed to other criteria) is a primary parent agent attribute in the model. This aspiration attribute is used as a means to constrain the location and movement of parent agents within the modelled environment. Results indicate that these location and movement constraints are needed to generate empirical patterns, and that patterns are generated most closely and consistently when schools agents differ in their ability to increase pupil attainment. Analysis of model output for simulations using these mechanisms shows how parent agents with above-average \" but not very high \" aspiration fail to get their child a place at their preferred school more frequently than other parent agents. We highlight the kinds of alternative school-place allocation rules and education system policies the model can be used to investigate.

Modeling the Transition to Public School Choice

Spiro Maroulis, Eytan Bakshy, Louis Gomez and Uri Wilensky
Journal of Artificial Societies and Social Simulation 17 (2) 3

Kyeywords: Public Policy, Education, School Choice
Abstract: We develop an agent-based model that captures the dynamic processes related to moving from an educational system in which students are automatically assigned to a neighborhood school to one that gives households more choice among existing and newly formed public schools. Analysis of our model reveals the importance of considering the timing of the entrance of new schools into the system in addition to their quantity and quality. Our model further reveals a range of conditions where the more households emphasize school achievement relative to geographic proximity in their school choice decision, the lower the mean achievement of the district \" a paradoxical mismatch between micro- and macro-levels of behavior. We use data from Chicago Public Schools to initialize the model.

Parental Choices and Children’s Skills: An Agent-Based Model of Parental Investment Behavior and Skill Inequality Within and Across Generations

Andrés Cardona
Journal of Artificial Societies and Social Simulation 17 (4) 8

Kyeywords: Skill Formation, Parental Investments, Inequality in the Life Course, Intrahousehold Allocation of Resources, Agent Behavior
Abstract: An agent-based simulation model (ABM) is developed and implemented using Python to explore the emergence of intragenerational and intergenerational skill inequality at the societal level that results from differences in parental investment behavior at the household level during early stages of the life course. Parental behavior is modeled as optimal, heuristic-based, or norm-oriented. Skills grow according to the technology of skill formation developed in the field of economics, calibrated with empirically estimated parameters from existing research. Agents go through a simplified life course. During childhood and adolescence, skills are produced through parental investments. In adulthood, individuals find a partner, give birth to the next generation, and invest in offspring. Number and spacing of children and available resources are treated as exogenous factors and are varied experimentally. Simulation experiments suggest that parental decisions at the household level play a role in the emergence of inequality at the societal level. Being egalitarian or not is the most important distinction in parental investment behavior, while optimizing parents generate similar results as egalitarian parents. Furthermore, there is a tradeoff between equality at home and inequality at the macro-level. Changes in the environment reduce or exacerbate inequality depending on parental investment behavior. One prediction of the model on intragenerational inequality in cognitive skills was validated with the use of empirical data. The simulation can best be described as a middle-range model, informed by research on skill formation and the intrahousehold allocation of resources. It is a first step toward more complex ABMs on inequality from a life course perspective. Possible model extensions are suggested. The Overview, Design Concepts, and Details (ODD) protocol and Design of Experiments (DOE) were used to document the model and set up the experimental design respectively.

High Standards Enhance Inequality in Idealized Labor Markets

Károly Takács and Flaminio Squazzoni
Journal of Artificial Societies and Social Simulation 18 (4) 2

Kyeywords: Discrimination, Labor Market Mismatch, Dual Matching, Aspirations, Sampling Bias
Abstract: We built a simple model of an idealized labor market, in which there is no objective difference in average quality between groups and hiring decisions are not biased in favor of any particular group. Our results show that inequality in employment emerges necessarily also in such idealized situations due to the limited supply of high quality individuals and asymmetric information. Inequalities are exacerbated when employers have high standards and keep only the best workers in house. We found that ambitious workers get higher quality jobs even if ambition does not correlate or even negatively correlates with internal quality. Our findings help to corroborate empirical findings on higher employment discrepancies in high rather than low status jobs.

Interpreting School Choice Treatment Effects: Results and Implications from Computational Experiments

Spiro Maroulis
Journal of Artificial Societies and Social Simulation 19 (1) 7

Kyeywords: Public Policy, Education, School Choice, Causal Inference
Abstract: Providing parents and students a choice to attend schools other than their assigned neighborhood school has been a leading theme in recent education reform. To evaluate the effects of such choice-based programs, researchers have taken advantage of the randomization that occurs in student assignment lotteries put in place to deal with oversubscription to popular schools and pilot programs. In this study, I used an agent-based model of the transition to school choice as platform for examining the sensitivity of school choice treatment effects from lottery-based studies to differences in student preferences and program participation rates across hypothetical study populations. I found that districts with higher participation rates had lower treatment effects, even when there were no differences in the distributions of school quality and student preferences between districts. This is because capacity constraints increasingly limited the amount of students who are able to attend the highest quality schools, causing the magnitude of the treatment effect to fall. I discuss the implications of this finding for interpreting the results of lottery-based studies involving choice schools.

Agent-Based Simulation Models of the College Sorting Process

Sean Reardon, Matt Kasman, Daniel Klasik and Rachel Baker
Journal of Artificial Societies and Social Simulation 19 (1) 8

Kyeywords: Socioeconomic Inequality, College Sorting, College Admission, College Enrollment
Abstract: We explore how dynamic processes related to socioeconomic inequality operate to sort students into, and create stratification among, colleges. We use an agent-based model to simulate a stylized version of this sorting processes in order to explore how factors related to family resources might influence college application choices and college enrollment. We include two types of “agents”—students and colleges—to simulate a two-way matching process that iterates through three stages: application, admission, and enrollment. Within this model, we examine how five mechanisms linking students’ socioeconomic background to college sorting might influence socioeconomic stratification between colleges including relationships between student resources and: achievement; the quality of information used in the college selection process; the number of applications students submit; how students value college quality; and the students’ ability to enhance their apparent caliber. We find that the resources-achievement relationship explains much of the student sorting by resources but that other factors also have non-trivial influences.

Revising the Human Development Sequence Theory Using an Agent-Based Approach and Data

Viktoria Spaiser and David J. T. Sumpter
Journal of Artificial Societies and Social Simulation 19 (3) 1

Kyeywords: Agent-Based Simulation, Human Development Sequence Theory, Democratisation, Mathematical Modeling, Data Analysis, Inequality
Abstract: Agent-based models and computer simulations are promising tools for studying emergent macro-phenomena. We apply an agent-based approach in combination with data analysis to investigate the human development sequence (HDS) theory developed by Ronald Inglehart and Christian Welzel. Although the HDS theory is supported by correlational evidence, the sequence of economic growth, democracy and emancipation stated by the theory is not entirely consistent with data. We use an agent-based model to make quantitative predictions about several different micro-level mechanisms. Comparison to data allows us to identify important inconsistencies between HDS and the data, and propose revised agent-based models that modify the theory. Our results indicate the importance of elites and economic inequality in explaining the data available on democratisation.

Can Redistribution by Means of a Progressive Labor Income-Taxation Transfer System Increase Financial Stability?

Thomas Fischer
Journal of Artificial Societies and Social Simulation 20 (2) 3

Kyeywords: Financial Stability, Income and Wealth Inequality, Debt, Redistribution
Abstract: We present a model featuring heterogeneous households with a conspicuous consumption motive, in which inequality can decrease financial stability, and relate this behavior to the recent financial crisis in the USA. A natural policy conclusion would be to combat income inequality jointly with financial instability by means of a progressive system of taxes and transfers. We investigate this for the case of a simple flat tax system on labor income. The system succeeds in decreasing volatility in asset markets by decreasing the share of high income individuals participating in destabilizing speculation. However, the model provides some very cautious notes on redistribution. As a result of redistribution, all agents are worse off class-wise and accumulate large amounts of debt, posing another potential hazard to financial stability. The latter can be explained by the arms race property of relative consumption. Moreover, the decreased inequality of income (flow) is accompanied by an increased inequality of net-worth (stock).

Lattice Dynamics of Inequity

Gérard Weisbuch
Journal of Artificial Societies and Social Simulation 21 (1) 11

Kyeywords: Inequality, Dynamical Regimes, Lattice, Transitions, Tags
Abstract: We discuss a model of inequity based on iteration of the Nash multi-agent bargaining game on a lattice. Agent's choices are based on a logit function and gradual decay of memories of past profits. Numerical simulations demonstrate the stability of various dynamical regimes, such as disorder, fairness or inequity, according to parameters and initial conditions. When playing the game on a lattice i.e. using neighbouring agent interactions instead of random interaction among the whole agent population, one observes spatial domains and specific patterns in addition to the temporal convergence toward attractors observed when interactions involve any pair of agents. A result specific to the network topology is the co-existence of domains with different regimes, allowing the emergence of the inequity condition even in the absence of tags.