This paper presents an agent-based model to investigate interactions between wind farm developers and landowners. Wind farms require hundreds of square miles of land for development and developers typically interact with landowners to lease land for construction and operations. Landowners sign land lease contracts without knowing the turbine layout, which affects aesthetics of property as well as value of the lease contract. Having a turbine placed on one's land is much more lucrative than alternative land uses, but landowners must sign over the use of their land without knowing whether they will receive this financial benefit or not. This process, typically referred to as “Landowner Acquisition,” is highly uncertain for both stakeholders—a source stated up to 50% of wind projects fail due to landowner acquisition issues. We present an agent-based model to study the landowner acquisition period with unique decision-making characteristics for nine landowners and a developer. Citizen participation is crucial to the acceptance of wind farms; thus, we use past studies to quantify three actions a developer can take to influence landowners: (1) community engagement meetings, (2) preliminary environmental studies, and (3) sharing the wind turbine layout with the landowner. Results show how landowner acceptance rates can change over time based on what actions the developer takes. While still in the “proof of concept” stage, this model provides a framework for quantifying wind stakeholder interactions and potential developer actions. Suggestions for how to validate the framework in the future are included in the discussion.

This paper presents an agent-based model to investigate interactions between wind farm developers and landowners. Wind farms require hundreds of square miles of land for development and developers typically interact with landowners to lease land for construction and operations. Landowners sign land lease contracts without knowing the turbine layout, which affects aesthetics of property as well as value of the lease contract. Having a turbine placed on one’s land is much more lucrative than not, but landowners have to sign over the use of their land without knowing if they will receive this financial benefit or not. The timing of this process, typically referred to as “Landowner Acquisition,” introduces high uncertainty for both stakeholders and represents a major pain point of the industry — a source stated up to 50% of wind projects fail due to landowner acquisition issues. We present an agent-based model that models the land lease contract period with unique decision-making characteristics for a set of landowners and a wind farm developer. Citizen participation is an integral part of community acceptance of wind farms, thus we use principles from past studies to quantify three actions a developer can take to influence landowner decisions: (1) community engagement meetings; (2) preliminary environmental studies; and (3) sharing the wind turbine layout with the landowner. The results show how landowner acceptance rates can potentially change over time based on what actions the developer takes. Overall, developers can use this model to better understand interactions with landowners and determine what actions may help positively influence landowner acceptance rates.

A customer journey map is a visual representation of the process that a person goes through when interacting with a product or a service and it is often related to human-centered design. The process of which customer journey maps are built is referred to as customer journey mapping (CJM) and traditionally this process includes techniques such as observations, interviews, and surveys. However, the emergence of new data collection techniques such as interactive mobile applications has made richer data available for service designers. This emerging data availability poses both challenges and opportunities for CJM. In this paper, we propose an innovative stochastic-based method to tackle these challenges while preserving the advantages of traditional CJM. Specifically, the proposed method models user-generated customer experiences as Markov chains and amalgamate the large quantities of experiences into a small number of customer journey maps which are easier to analyze for service designers. This method is based on a clustering algorithm that achieves the maximum posterior likelihood post-clustering. By employing our method on collected data, service designers can discover key components in customers’ experiences in addition to some edge cases. We will demonstrate the effectiveness of this method using sample data from the 2017 National Household Travel Survey (NHTS).

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The Europa mission approved in 2019 is still in the development phase. It is designed to conduct a detailed reconnaissance of that moon of Jupiter as it could possibly support life as we know it. This article is based on a top-down approach (mission → system → subsystems → components) to model the probability of mission failure. The focus here is on the case where the (uncertain) radiation load exceeds the (uncertain) capacity of critical subsystems of the spacecraft. The model is an illustrative quantification of the uncertainties about (1) the complex external radiation environment in repeated exposures, (2) the effectiveness of the shielding in different zones of the spacecraft, and (3) the components' capacities, by modeling all three as dynamic random variables. A simulation including a sensitivity analysis is used to obtain the failure probability of the whole mission in forty-five revolutions around Jupiter. This article illustrates how probabilistic risk analysis based on engineering models, test results and expert opinions can be used in the early stages of the design of space missions when uncertainties are large. It also describes the optimization of the spacecraft design, taking into account the decisionmakers' risk attitude and the mission resource constraints.