This paper presents a research framework for studying peer effects in the diffusion of innovations. The underlying mechanisms of peer effects are generally under-discussed in existing studies. By investigating diffusion processes in the real world and reviewing previous studies, we find that information transmission, experience sharing and externalities are the basic mechanisms through which peer effects occur. They are termed as information effect, experience effect and externality effect, respectively. The three effects could occur through different types of relationships in a social network. Each of them plays a different role at different stages of a diffusion process. A simulation model incorporating multiple effects in a multiplex network is developed to provide a theoretical study. We simulate the experience effect and the externality effect in a context of rural diffusion. It generates the widely acknowledged pattens of diffusion in various scenarios. The experiments conducted using the model show that peer effects as a whole can be substantially misestimated if the underlying mechanisms are ignored.

This case illustrates how a mixed-methods approach facilitates the study of social interactions at the individual level. Using the case of the diffusion of a new crop in rural villages, it describes the collection and analyses of the data reflecting farmers' adoption behavior and the data representing the social networks in which adoption takes place. It is often challenging to obtain a workable complete dataset of micro-level behavioral data and social network data. This case shows how to collect these data in an approach with pragmatic strategies that use primary and secondary research jointly. Specifically, it illustrates the collection of the data of various ascribed social relationships using interviews and pre-existing documents and the data of adoption behavior using questionnaire and historical records.

English Abstract: An individual's behaviour to adopt an innovation can be influenced by other members in the same group. Such influence is referred to as peer effects in literature. However, systematical examination on the causal mechanisms of peer effects has not found in existing studies. Based on a definition of peer effect, this paper presents a theoretical framework to study peer effects in the diffusion of innovations. Peer effects are split into information effect, experience effect and externality effect. We apply this framework in the case of the diffusion a high-value crop in Golden Rooster Village in Wuhan. We find that each of the three effects plays a dominant role in the early, intermediate and late phase of the diffusion process, respectively, and takes place through different social relationships. Information effect induces the adoption of a small number of innovators, but not large-scale diffusion. Experience effect plays a relatively more significant role and leads to the formation of “critical mass”. Externality effect then coerces the “loggers” to follow suit.

The structure of rural societies in China has been studied by many researchers. Several characteristics are widely viewed as the unique features of Chinese rural societies. However, the quantitative measure of these characteristics are not found. We measure and examine the structure of Chinese rural communities from the perspective of social networks. We collected solid data of four important social relationships, namely, the kinship, the house neighbourhood, the land plot neighbourhood and the political relationship from ten natural villages (which constitute an administrative village) in central China. We calculate the network statistics and topological properties of the networks in the villages. Several characteristics are found. (1) Kinship and geographical relationship are the two major relationships that constitute the social networks in Chinese villages. (2) Chinese villages exhibit prominent small-world property. (3) The villages are generally quite decentralised. (4) Relatives, especially close relatives tend to also live geographically close to each other.

As a conceptual framework for understanding contemporary sustainability challenges, telecoupling emphasises the importance of socioeconomic and environmental interactions over long distances. These long-distance interactions can occur through multiple human activities. Here we focus on international trade, a major channel of telecoupling flows, and in particular on the international trade of metals. We present a conceptual model to show how trade can be viewed through the telecoupling framework. We then use world input-output tables to quantitatively examine how countries contribute to both economic and environmental flows through trade of metals, but also how that contribution varies depending on their position in the global value chain of contemporary international trade. This analysis is built on recently-developed techniques for decomposing gross exports of physical products and embedded environmental assets as well as previous methods for valuing environmental assets. We make comparisons between countries' contributions to flows of economic value vs embedded greenhouse gas emissions, but also examine contributions beyond total volumes of trade and bilateral trade. Specifically, we quantify the economic and environmental 'spillover' effects that occur in contemporary international trade due to the global value chain in which flows of intermediate goods form components in other subsequently traded goods. We interpret differences between countries' contributions to the flows of economic value versus embedded environmental assets as being related to the intensity and efficiency of resource use during production. In turn, differences in contributions to direct trade flows versus spillover flows are related to their positions in the global value chain. Subsequently, we discuss other elements of the telecoupling framework in trade - agents, causes and effects. Quantitatively incorporating these telecoupling framework elements alongside spillover flows will enable investigation of dynamics and relationships that traditional trade theories, data and models do not currently account for well.

The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers' decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers' decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers' behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers' decision-making: the multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers' emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural systems.

We present an agent-based model to test two competing hypotheses in the theory of self-enforcing agreement. In cooperative teams (such as agricultural cooperatives), self-enforcing agreement plays a critical role in guaranteeing members' work incentives when the monitoring from a third party is absent. In order to provide an effective sanction to the violators so as to maintain the agreement, two seemingly conflicting strategies are proposed. One is allowing the members to exit the team freely. The other is imposing a high exit cost to restrict members from leaving the team. The arguments behind each strategy are elaborated in Lin (1993) and Dong and Dow (1993), respectively. However, these strategies have never been tested in the same model. In fact, no formal model is presented for one of the arguments. To fill this gap, we develop a model that incorporates the two arguments as two scenarios in a shared framework. Our model takes heterogeneity of team members (e.g., their laziness, work ability and patience to future well-being) into consideration, which allows us to better understand the divergence of these two arguments. We find the two arguments essentially claim different consequences under different conditions of members' characteristics and team size. Our study demonstrates agent-based simulation can be an effective approach of testing game theoretical arguments and exploring game theoretical ideas.

We examine how adoption of a high-value crop diffuses through social networks, using detailed demographic, social network, and adoption data from ten villages in Central China. We develop a model of diffusion through a multiplex network that distinguishes the influence of sharing experiential resources by earlier adopters and that of externalities due to adoption behaviours. We find that the sharing of resources among family members and the production externalities arose between contiguous land plots both significantly influence farmers' adoption. Furthermore, the sharing of resources is more influential in the early stage of diffusion process, whereas the externalities mainly matter in the late stage. We also find that adopters give priority to those with a stronger kinship tie when deciding with whom to share their resources, and proximity in age can strengthen the kinship ties.

The influence of peer effects on the diffusion of innovations has been extensively studied. However, the underlying mechanisms of peer effects are generally understudied. Gaps in this knowledge could lead to misestimation of peer effects and inefficient interventions. This study examined the role of three types of specific peer effects -- information effect, experience effect, and externality effect -- in the adoption of innovation in rural China. By referring to the diffusion process of a rural innovation, we developed a simulation model that incorporated multiple peer effects on a multiplex network. The model allowed us to estimate the influence of each specific effect and to investigate the interplay of the positive and negative directions of the effects. The main results of simulated experiments were the following: (1) a negative information effect in the system caused the diffusion of innovation to vary around a middle-level rate, which resulted in a fluctuating diffusion curve rather than a commonly found S-shaped one; (2) in the case of full diffusion, experience effect significantly shaped the diffusion process at an earlier stage, while externality effect mattered more at a later stage; and (3) network properties (i.e., connectivity, transitivity, and network distance) provided indirect influence on diffusion through specific peer effects. Overall, our study illustrated the need to understand specific underlying causal mechanisms when studying peer effects. Simulation studies provide an effective approach to generate such understanding.