Firouzeh Taghikhah

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Firouzeh Taghikhah

Institution

University of Technology Sydney

ORCID more info

https://orcid.org/0000-0002-0851-6816

GitHub more info

No associated github account.

No bio entered.

Research Interests

  • System modelling of behavior change
  • Socio-environmental systems for sustainable development
  • Life cycle analysis
  • Serious games for sustainable future
  • Food preferences
  • Agricultural economics

Based on theoretical and empirical considerations, ORVin is developed to understand consumer purchasing behavior regarding organic wines. To gain insight into the process of wine consumption, the theory of planned behavior is considered along with alphabet theory and goal framing theory. This provides a solid theoretical framework for identifying behavioral factors including beliefs, attitudes, norms, habits, and goals that may influence organic wine purchases. The model can be used to examine the effectiveness of different interventions for encouraging households to purchase organic wine instead of conventional wine. ORVin provides a dynamic platform to study the individual reaction of the disaggregated, low-level actors of the system to the hypothetical changes in the wine market such as taxation, marketing campaigns, and promotions. The cumulative impacts of changing behavior are also evaluated with respect to the environment. This model improves users understanding of the complexity of wine purchasing decisions and help them to further interpret and forecast organic wine market.

This model simulates the interplay between consumer preferences and socio-environmental issues related to agriculture and food production. We operationalize the novel concept of extended agro-food supply chain and simulate adaptive behavior of farmers, food processors, retailers, and customers. Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. We propose an integrated approach combining agent-based, discrete-event, and system dynamics modeling for a case of wine supply chain.

This model simulate the interplay between consumer preferences and socio-environmental issues related to agriculture and food production. We operationalize the novel concept of extended agro-food supply chain and simulate adaptive behavior of farmers, food processors, retailers, and customers. Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. We propose an integrated approach combining agent-based, discrete-event, and system dynamics modeling for a case of wine supply chain.

Computational social science has seen a shift from theoretical models to empirical agent-based models (ABMs) grounded in data-driven correlations between behavioral factors defining agents’ decisions. The community’s interest in theoretical ABMs with behavioral theories setting stylized rules that guide agents’ actions is fading, especially when it concerns policy-related simulations. However, it remains unclear to what extent theory-driven ABMs mislead, if at all, possible policy decisions when compared to the outcomes of models with empirical micro-foundations. This is especially relevant for pro-environemntal policies that increasingly rely on quantifying cumulative effects of individual behavioral changes, where ABMs are so useful. To address this methodological dilemma, we propose a comparison framework to quantitatively explore the differences between theory- and data-driven ABMs. Inspired by the existing theory-based model, ORVin-T, which studies the individual choice between organic and conventional products, we designed a survey to collect data on individual preferences and purchasing decisions. We then used this extensive empirical microdata to build an empirical twin, ORVin-E, replacing the theoretical assumptions and secondary aggregated data used to parametrize agents’ decision strategies with empirical survey data. We compare the models in terms of key outputs, perform sensitivity analysis and three policy scenario.

Threats to sustainable food production are accelerating due to climate change, depletion of natural capital, and global financial instability. This causes significant risks to farmers, consumers, and financial and policy institutions. To better understand and forecast alternative futures for agricultural production, we have developed a dynamic simulation model that accounts for the core natural capital components of agro-ecosystems, including climate, soil, carbon, water, nitrogen, phosphorus, microorganisms, erosion, crops, grazing, and trees. Understanding agro-ecosystems, and how varying management styles impact long-term local and global risks, is critical to future wellbeing. The model can be used to simulate dynamics of soil health and project it into the future to assess vulnerabilities and resilience. This knowledge can inform and guide investment decisions by financial institutions, insurance companies, farmers, and government agencies. Here, we describe the basic model structure, sensitivity, and calibration results. We then run a few scenarios to highlight the model’s ability to analyze the results of alternative agro-ecosystem management options.

Under development.

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