A social interaction model with both in-group and out-group effects

The global warming problem due to greenhouse gas emission has become very serious. Moreover, in the transportation section, the construction of a sustainable transportation system in the environment has been an important subject. Not only improvement of service level of modes, except those of private vehicles, but also consciousness for the environmental problem of an individual trip maker is important for eco-commuting promotion. Eco-consciousness can be changed under the influence of other people. The multi-agent simulation system for planning of ecocommuting promotion is developed using a social interaction model for eco-consciousness in this study. Modal shift is modeled by considering the result of a transport social experiment for eco-commuting promotion as reference. Influence of the promotion plan such as improvement of service level of public transport, promotion of individual eco-consciousness, and strengthening the interaction of individuals can be analyzed using the proposed model. Finally, it is concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for the planning of eco-commuting promotion.

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Agents enhancing ?-aminobutyric acid receptor-coupled chloride ionophore function. Effects in a social interaction model of anxiety in the rat

Drug Development Research ◽

10.1002/ddr.430190103 ◽

1990 ◽

Vol 19 (1) ◽

pp. 13-21

Author(s):

Andrew P. Guy ◽

Colin R. Gardner

Keyword(s):

Social Interaction ◽

Interaction Model ◽

Aminobutyric Acid ◽

Acid Receptor ◽

Chloride Ionophore

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ENRICHING VOCABULARY THROUGH SOCIAL INTERACTION MODEL

EXPOSURE JURNAL PENDIDIKAN BAHASA DAN SASTRA INGGRIS ◽

10.26618/ejpbi.v4i1.921 ◽

2015 ◽

Vol 4 (1) ◽

pp. 40

Author(s):

Siti Maryam Hamid

Keyword(s):

Social Interaction ◽

Standard Deviation ◽

Multiple Choice ◽

Interaction Model ◽

T Test ◽

Choice Test ◽

Multiple Choice Test ◽

Significant Difference ◽

Post Test ◽

The Mean

The objective of this research was to find out the significance of the students’ achievement before and after learning vocabulary through Social Interaction Method at the eight class of SMP Guppi Samata Gowa. This research employed pre-experimental method with one group pretest and posttest design. There were two variables, namely dependent variable was the students’ vocabulary achievement and the independent variable was the application of Social Interaction Model in teaching vocabulary. The population was the students in the eighth grade of SMP Guppi Samata Gowa. The sample of the research consisted of 50 students which were taken by using cluster total sampling, 25 students were taken as an experimental class and 25 students were taken as a controlled class. The instrument was vocabulary test in the multiple-choice test. The multiple choice test consists of 10 items that consist of five choices. The findings of the research were students vocabulary used pre-test and post-test. The result of the data indicated that there was a significant difference between students’ post-test in experimental class and controlled class. The mean score of posttest (61.6) in experimental class was greater than the mean score of posttest (56) in controlled class and the standard deviation of posttest (8.94) in experimental class was greater than the standard deviation of posttest in controlled class (6.29). From t-test, the researcher found that the value of t-test (2.553) was greater than t-table (2.021) at the level of significance 0.05 with the degree of freedom (df) = 48.

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Trade, migration, and the dynamics of spatial interaction

10.31235/osf.io/trbf8 ◽

2019 ◽

Author(s):

Nicolas Gauthier

Keyword(s):

Social Interaction ◽

Settlement Patterns ◽

Large Scale ◽

Spatial Interaction ◽

Interaction Model ◽

Structural Features ◽

Small Scale ◽

Interaction Models ◽

Road Map ◽

Migration Flows

Archaeological settlement patterns are the physical remains of complex webs of human decision-making and social interaction. Entropy-maximizing spatial interaction models are a means of building parsimonious models that average over much of this small-scale complexity, while maintaining key large-scale structural features. Dynamic social interaction models extend this approach by allowing archaeologists to explore the co-evolution of human settlement systems and the networks of interaction that drive them. Yet, such models are often imprecise, relying on generalized notions of settlement "influence" and "attractiveness" rather than concrete material flows of goods and people. Here, I present a dis-aggregated spatial interaction model that explicitly resolves trade and migration flows and their combined influence on settlement growth and decline. I explore how the balance of costs and benefits of each type of interaction influence long-term settlement patterns. I find trade flows are the strongest determinant of equilibrium settlement structure, and that migration flows play a more transient role in balancing site hierarchies. This model illustrates how the broad toolkit for spatial interaction modeling developed in geography and economics can increase the precision of quantitative theory building in archaeology, and provides a road-map for connecting mechanistic models to the empirical archaeological record.

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Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity

Econometrica ◽

10.3982/ecta13660 ◽

2020 ◽

Vol 88 (5) ◽

pp. 2109-2146 ◽

Cited By ~ 3

Author(s):

Guido M. Kuersteiner ◽

Ingmar R. Prucha

Keyword(s):

Social Interaction ◽

Asymptotic Distribution ◽

Network Formation ◽

Interaction Model ◽

Interactive Effects ◽

Time Varying ◽

Cross Sectional ◽

Moment Conditions ◽

Panel Models ◽

Common Shocks

This paper considers a class of generalized methods of moments (GMM) estimators for general dynamic panel models, allowing for weakly exogenous covariates and cross‐sectional dependence due to spatial lags, unspecified common shocks, and time‐varying interactive effects. We significantly expand the scope of the existing literature by allowing for endogenous time‐varying spatial weight matrices without imposing explicit structural assumptions on how the weights are formed. An important area of application is in social interaction and network models where our specification can accommodate data dependent network formation. We consider an exemplary social interaction model and show how identification of the interaction parameters is achieved through a combination of linear and quadratic moment conditions. For the general setup we develop an orthogonal forward differencing transformation to aid in the estimation of factor components while maintaining orthogonality of moment conditions. This is an important ingredient to a tractable asymptotic distribution of our estimators. In general, the asymptotic distribution of our estimators is found to be mixed normal due to random norming. However, the asymptotic distribution of our test statistics is still chi‐square.

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