Decision Maker's Preferences Research Articles

Optimal sizing for wind-photovoltaic-hydrogen storage integrated energy system under intuitionistic fuzzy environment

As the primary consideration, sizing optimization has great impact on wind-photovoltaic-hydrogen storage integrated energy system (WPHIES) construction. However, few studies on sizing optimization consider the characteristics of renewables coupled with hydrogen storage, or discuss decision-making fuzziness. Therefore, a three-objective mixed integer linear programming model for WPHIES sizing is proposed under intuitionistic fuzzy environment. Based on the concept of Energy Trilemma, indexes of the annual comprehensive cost, the annual carbon emission and the external electricity dependence are selected as the optimization objectives. Next, considering that exact numerical evaluation cannot well reflect the uncertainty of decision-making preferences, the intuitionistic fuzzy number is introduced to determine index weights so as to improve the accuracy of decision-making. At last, a case study including comparison, sensitivity and scenario analyses is carried out. Compared with traditional integrated energy system, WPHIES can achieve 15% annual comprehensive cost, 26% annual carbon emission and 84% external electricity dependence of reduction. Furthermore, great priority should be given to photovoltaic so as to promote renewable energy development. This research can not only provide great reference for scholar to explore issues of WPHIES sizing optimization, but also help WPHIES investors or managers efficiently and scientifically arrive at optimal sizing schemes.

Cognitive Mapping and Framing Bias on Decision Making

The purpose of this study is to test and provide empirical evidence regarding causal cognitive mapping techniques in reducing framing bias in decision making. In this study, the bias that occurs can be measured through the risk preferences of decision makers, namely the tendency to be risk averse or risk seeking. This study used a 2x2 between subjects’ experimental design. The results of the research show that both hypotheses are supported. When information is presented in positive framing, the decision maker's risk preference tends to be risk averse, whereas if information is presented in negative framing, the decision maker's risk preference tends to be risk seeking. The study shows that the causal cognitive mapping technique's ability to reduce or reduce bias due to framing (information framing) of available decision alternatives. The implication is that decision makers should think harder in processing the available information. Causal cognitive mapping technique can assist decision makers in connecting various factors that are considered relevant in a problem.

Wireless Distributed Consensus in Vehicle to Vehicle Networks for Autonomous Driving

Vital societal and industrial autonomous components are increasingly interconnected through communication networks to complete critical tasks cooperatively. However, as the reliability and trust requirements for connected autonomous systems continue to grow, the centralized communication and decision approaches that are in use today are reaching their limits. Focusing on autonomous driving applications, this paper proposes a resilient and trustworthy framework on wireless distributed consensus networks, where the communication links are less reliable or are even in the presence of incorrect local sensor readings/decisions. To accomplish that, a novel three stages consensus mechanism is proposed based on the practical Byzantine fault tolerance (PBFT), where the veto collection and gossip stages are designed to meet the stringent and complex requirements for a vehicle's maneuvers. A plan tree synthesis is also proposed to make consensus on a series of decisions while adopting network members' decision preferences. A detailed protocol including the distributed consensus, plan tree synthesis, dynamic grouping, etc. is proposed. Simulation results show that the proposed consensus mechanism is able to be reached and propagated through the network under poor wireless communication conditions and the presence of faulty vehicles with incorrect sensor readings. The result can be extended to other autonomous systems to significantly enhance safety in critical industrial applications.

P.013 Patient preferences for selection of preventive migraine therapies in Canada: results from a discrete choice experiment

Background: This study assessed the importance of mode of administration relative to other treatment attributes when selecting a preventive migraine therapy. Methods: Cross-sectional study among Canadian adults diagnosed with migraine with ≥5 monthly migraine days and tried ≥2 prescription migraine treatments (any kind/duration). Preferences for treatments varying in the following attributes were evaluated via a discrete choice experiment: speed of efficacy (effective in 24hr/1wk/3mo), duration of efficacy (wears off never/1wk/2 wks before next dose), mode of administration (infusion/auto-injection/cranial injections), administration setting (clinic/home), and administration frequency (1mo/3mo). Attribute-level preference weights were estimated using Hierarchical Bayes modeling. Results: Of 200 respondents, 142 experienced episodic migraine and 58 experienced chronic migraine. Preference weights confirmed that respondents’ most preferred treatments were those that provided fast and long-lasting efficacy (effective in 24hr = 0.59; wears off never = 1.07) and were offered via infusion (0.58) or auto-injection (0.47) over intracranial injection (-1.04). Respondents reported being moderately willing to receive infusions in either a home or clinic setting (1-6 Likert scale from “not at all” to extremely” willing). Conclusions: Second to speed and duration of efficacy, respondents were most concerned with mode of administration when selecting their preferred migraine preventive, suggesting that physicians should consider patient preferences in treatment decision-making.

Hesitant fuzzy β-covering ( T , I ) rough set models: An application to multi-attribute decision-making

Covering rough sets have been successfully applied to decision analysis because of the strong representing capability for uncertain information. As a research hotspot in decision analysis, hesitant fuzzy multi-attribute decision-making (HFMADM) has received increasing attention. However, the existing covering rough sets cannot handle hesitant fuzzy information, which limits its application. To tackle this problem, we set forth hesitant fuzzy β-covering rough set models and discuss their application to HFMADM. Specifically, we first construct four types of hesitant fuzzy β-covering ( T , I ) rough set models via hesitant fuzzy logic operators and hesitant fuzzy β-neighborhoods, which can handle hesitant fuzzy information without requiring any prior knowledge other than the data sets. Then, some intriguing properties of these models and their relationships are also discussed. In addition, we design a new method to deal with HFMADM problems by combining the merits of the proposed models and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. In this method, we not only consider the risk preferences of decision-makers, but also present a new hesitant fuzzy similarity measure expressed by hesitant fuzzy elements to measure the degree of closeness between two alternatives. Finally, an enterprise project investment problem is applied to illustrate the feasibility of our proposed method. Meanwhile, the stability and effectiveness of our proposed method are also verified by sensitivity and comparative analyses.

Towards sustainable last-mile logistics: A decision-making model for complex urban contexts

E-commerce growth is raising the demand for logistic activities, especially in the last-mile, which is considered the most ineffective part of the supply chain and a negative externalities source. Although various solutions aim to address these issues, selecting the best one is challenging due to multiple perspectives, conflicting criteria, trade-offs, and complex and sensitive urban contexts. This article proposes a 4-level hierarchical model based on the triple bottom line of sustainability that may assist decision-makers in selecting the most adequate last-mile solution for historic centers. The model was defined based on a systematic literature review; evaluated by interviewing a set of experts; and quantified according to an AHP-TOPSIS approach. This quantification focused on the historic center of Porto, Portugal. The experts considered all three sustainability dimensions similarly important. Air pollution was the most valued sub-criterion whereas Visual pollution was the least. 67 decision-maker profiles were defined, showing that environmentally oriented decision-makers prefer cargo bikes, while decision-makers who prioritize economic and social factors prefer parcel lockers. All last-mile solutions considered in the model yielded similar results, therefore suggesting a combined distribution strategy. Nevertheless, the use of parcel lockers is the most favorable solution for Porto’s historic center.

Impact of social media messages on perceived risk and severity of adverse effects and regret of HPV vaccination: The moderating role of decision-making preference.

10543 Background: The HPV vaccine has proven to be a highly effective and safe public health intervention for reducing the risk of various types of cancer. Despite its efficacy and safety, misinformation and negative opinions about the vaccine on social media can lead to negative emotions such as regret among those who have received it. This study investigated the impact of different content of social media messages on perceived risk and severity of adverse effects and regret of HPV vaccination. Methods: An experiment was designed to expose participants to messages posted on social media that manipulated the number of Vaccine Adverse Effects and the appeal used (narrative vs. statistics). The study measured the effect of the message on decisional regret and also assessed preferred level of involvement with medical decision-making. The online study was implemented among college students enrolled in a Midwestern or a South Texas university. We used structural equation modeling (SEM) to systematically test the relationships between the variables of interest. Results: A total of 721 participants were exposed to the experimental materials and completed the questionnaire. The results of the SEM (with overall good fitness of RMSEA = 0.015, CMIN/df = 1.172) suggest that messages that contained more numeric information (standardized regression weights [Std Reg Wght = 0.161]) and more adverse effects of the HPV vaccine (Std Reg Wght = 0.076) led to a higher perception of the risk of vaccine adverse effects. The severity of adverse effects was significantly associated with the number of side effects in the message (Std Reg Wght = 0.147). This increase in perceived risk (Std Reg Wght = 0.182) and severity of side effects of the vaccine (Std Reg Wght = 0.163) seem to increase decisional regret. However, this relationship was moderated by individuals’ preferences for making their own medical decisions. Individuals with a higher preference to make their own health decisions were less likely to experience decisional regret (Std Reg Wght = -0.238). Conclusions: Messages on social media can exacerbate negative emotions and lead to hesitation in following future medical advice. Encouraging individuals to play a more active role in their medical decision-making appears to reduce this negative emotion and increase their confidence in health decisions.

A Novel Diversified Attribute Group Decision-Making Method Over Multisource Heterogeneous Fuzzy Decision Systems With Its Application to Gout Diagnosis

Group decision-making (GDM) is a useful uncertain decision theory and tool. With the evolution of time, GDM problems become more and more complex. In reality, there are plenty of GDM problems with diversified attributes and heterogeneous information. In this paper, the diversified attributes describe the differences in attributes given by different decision-makers, and heterogeneous information refers to the differences in data types. Nonetheless, few existing GDM studies simultaneously focus on the above two characteristics of GDM problems. Moreover, the scenario of multiple decision preferences, and the attribute and expert weights based on heterogeneous information are also seldom discussed. Given this, by considering a kind of GDM problem with the characteristics of multi-source, heterogeneity, diversity, and multiple fuzzy decisions, this paper builds a novel GDM method from the perspective of multigranulation rough sets (MGRSs). Firstly, we construct a new weighted multigranulation generalized fuzzy rough set (WMGGFRS) over multi-source heterogeneous fuzzy decision systems. Subsequently, in response to heterogeneous information, the extended entropy weight methods are given to calculate the weight of each attribute and the weight of each expert. After that, we establish a GDM method using the WMGGFRS model to realize multi-source information fusion. Lastly, we apply the established method to the management decision-making problem of gout diagnosis. The results of comparative and sensitivity analyses demonstrate the feasibility, effectiveness, stability, and superiority of the method.

МЕТОДЫ НЕЧЕТКОГО МНОГОКРИТЕРИАЛЬНОГО ГРУППОВОГО ПРИНЯТИЯ РЕШЕНИЙ ДЛЯ ЗАДАЧ ЭВАКУАЦИИ ПРИ ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ

The purpose of this article is to analyze the current state of research in the field of fuzzymulticriteria optimization methods, as well as the development of aggregation operators and algorithmsusing fuzzy multicriteria group decision-making using an intuitionistic attitude of linguisticpreferences. The most well-known fuzzy methods of multicriteria optimization are presented:ELECTRE, PROMETHEE, VIKOR, TOPSIS, AHP, ANP, MACBETH, DEMATEL, Shoke integraland DEA, their features, applications and the most cited articles are considered. Most real optimizationproblems may have conflicting goals. The method of fuzzy multi-purpose decision-makingFMODM is also presented for situations where there are inaccuracies and uncertainty in somegoals and variables on which they depend; methods of fuzzy multi-purpose linear programmingFMOLP, fuzzy multi-objective target programming FMOGP and fuzzy heuristic decision-makingmethods. The problem of fuzzy multicriteria group decision-making during evacuation with anintuitive relation of linguistic preferences is considered. It is noted that fuzzy logic methods areparticularly suitable for making evacuation decisions when there is little data, knowledge ofcause-and-effect relationships is inaccurate, and observations and criteria can be expressed inlinguistic qualitative terms. The main stages of group making the best decision among alternativesin a fuzzy environment are presented: combining expert assessments; obtaining a final assessmentfor each alternative represented by a linguistic variable; ranking alternatives; group making themost preferred decision. An approach to group decision-making with an intuitive preference relationshipbased on aggregation procedures is proposed. The group model of decision-making andthe concept of fuzzy group decision and linguistic variables used in predicting an emergency situationand planning evacuation are considered. It is noted that the well-known operators of orderedweighted averaging OWA, LOWA do not take into account the weights of experts. The Low operatoris defined, which allows taking into account the weight values of experts, as well as an approachto determining a fuzzy group solution of aFCS as a type 2 set. Algorithms for determininga fuzzy group multicriteria solution based on aFCS are presented

Multi-objective optimal maintenance strategy considering imperfect preventive maintenance: A case study on railway VOBC

Preventive maintenance procedures for systems are designed to improve long-term operating performance and reduce maintenance costs. This paper attempts to seek a trade-off between system reliability and maintenance costs and explores the optimal multi-objective maintenance strategy applicable to different field situations. We first provide an overview of the problem and analyze the impacts of varying different maintenance activities on reliability. Then, a mathematical model of reliability losses and maintenance costs is defined and derived, which will be used to construct a multi-objective maintenance strategy. In this research, the PSO-SA stochastic optimization algorithm is proposed to discover the Pareto frontier to find the optimal preventive maintenance threshold and inspection interval that minimizes reliability loss and maintenance costs. Finally, a case study is performed with railway VOBC as an illustration. The analysis results illustrate that the proposed multi-objective maintenance strategy can satisfy the preferences of various decision-makers. It is suitable for different maintenance needs and can support the development of on-site maintenance strategies.

Optimal Location of Emergency Facility Sites for Railway Dangerous Goods Transportation under Uncertain Conditions

Railroad accidents involving dangerous goods (DG) need to be rescued quickly due to their hazardous nature. This paper proposes an emergency facility location model for the railway dangerous-goods transportation problem (RDGT-EFLP, abbreviated as EFLP). The EFLP model is based on an ellipsoidal robust model that introduces a robust control safety parameter Ω to measure the risk preferences of decision makers and limits the range of uncertain demand, the range of uncertain service and the range of safety parameters to find the solution for siting emergency facilities, when the time and location of emergency events are unknown. The model is solved using a genetic algorithm (GA) and real data after abstraction. Finally, a comprehensive analysis of the solution results under different maximum overcoverages illustrates the feasibility and effectiveness of the model.

Patients' preferences in therapeutic decision-making in digestive oncology: a single centre cross-sectional observational study

Considering the preferences in Shared Decision Making (SDM) of patients with Digestive Cancer (DC) is crucial to ensure the quality of care. To date, there is limited information on preferences in SDM of patients with DC. The objectives of this study were to describe digestive cancer patients’ preference for involvement in therapeutic decision-making and to identify variables associated with these preferences. An observational prospective study in a French university cancer center has been conducted. Patients completed two questionnaires to qualify and quantify their preference for involvement in therapeutic decision-making: the Control Preference Scale (CPS) and the Autonomy Preference Index (API), which is composed of the Decision Making (DM) score and the Information Seeking (IS) score. Associations between these scores and socio-demographic data, disease-related data, coping strategies (Brief-COPE), physical (QLQ-C30) and psychological (HADS) quality of life were tested. One-hundred fifteen patients returned the questionnaires. The majority of patients reported a passive (49.1%) or a collaborative (43.0%) CPS status. The mean DM score was 39.4 Variables associated with decision-making preferences were occupational status and time since diagnosis. The identification of variables associated with patients' preferences for involvement in decision-making can help make clinicians aware of patients' needs and wishes. However, it can only be determined by interviewing the patient individually.

The influence of temporal focus on individual intertemporal decision-making in life history strategy framework

Temporal factors have an important role in intertemporal decision-making. Temporal focus is the extent to which individuals characteristically direct their attention to the past, present, and/or future. Temporal focus can influence resource allocation. It was not known, however, whether temporal focus might affect intertemporal decision-making. The purpose of this study is to investigate how life history strategies and temporal focus together influence intertemporal decision-making. Study 1 used a questionnaire to preliminarily explore the relationship between temporal focus and intertemporal decision-making. Results showed that temporal focus was positively associated with individuals' intertemporal decision preferences, and the more individuals preferred future focus, the higher their rate of temporal discounting. Study 2 examined the joint effect of the temporal focus and life history strategy on intertemporal decision-making by manipulating the temporal focus of individuals with different life history strategies. The manipulation of temporal focus influenced intertemporal decision preferences of fast strategy individuals, that is, the short-term small reward in the future focus and the long-term large reward in the past focus, but has no effect on the intertemporal decision preference of slow strategy individuals. The entire study confirms life history theory and validates the role of perceptual time-based models in intertemporal decision-making.

A Context-Sensitive Alternative to Hick’s Law of Choice Reaction Times: A Mathematical and Computational Unification of Conceptual Complexity and Choice Behavior

Hick’s law describes the time that individuals take to make a preference decision when presented with a set of possible choices. Basically speaking, the law states that decision time is a logarithmic function of the number of choices when the choices are equiprobable. However, the evidence examined here suggests that this, and a variant of the law for non-equiprobable choices based on Shannon entropy, are not effective at predicting decision reaction times involving structured sets of alternatives. The purpose of this report is to communicate a theoretical alternative to Hick’s law that is derived from a mathematical law of invariance for conceptual behavior at the heart of Generalized Invariance Structure Theory (Vigo, 2013, 2015). We argue that such an alternative accounts more precisely for decision reaction times on structured sets. Furthermore, we argue that Hick’s law is a special case of this more general law of choice reaction times for categories with zero degree of invariance.

Research on COVID-19 Vaccine Supply Chain Disruption Recovery Strategy from Resilience Perspective

The COVID-19 pandemic has greatly exacerbated the uncertainty of the external environment, meanwhile, the COVID-19 vaccine supply chain is highly complex and fragile, so its probability of disruption is higher and it is more difficult to recover, resulting in delays and shortages of vaccine supply. In order to develop the disruption recovery strategy of the COVID-19 vaccine supply chain and optimize the disruption recovery process, a multi-objective model was established and solved. Taking the shortest disruption recovery time, the largest reliability of the supply chain system, and the lowest recovery cost as the objective function, and taking the four dimensions of resilience, namely redundancy, robustness, resourcefulness and agility, as the decision variables, the model uses the nondominated ranking genetic algorithm with elite strategy (NSGA-II) to solve the problem, and uses the Pareto frontier analysis method to optimize the solution set, and the sensitivity analysis of the key parameters in the model is analyzed. Finally, the redundancy, robustness, resourcefulness and agility of the COVID-19 vaccine supply chain disruption recovery combination strategy under different decision-making preferences are given.

Patient engagement in decision making and associated factors among outpatients with selected non-communicable chronic diseases in public hospitals of West Shoa, Ethiopia.

Despite the importance of patient engagement in health care decision-making in the care of patients with chronic diseases, there is limited information about it and the factors affecting it in Ethiopia and in the Public Hospitals of West Shoa in particular. Thus, this study was designed to assess the engagement of patients with selected chronic non-communicable diseases in health care decision-making and associated factors in public hospitals of West Shoa Zone, Oromia, Ethiopia. We used an institution -based cross-sectional study design. We used systematic sampling for the selection of study participants from June 7-July 26, 2020. Standardized, pretested, and structured Patient Activation Measure was used to measure patient engagement in healthcare decision-making. We did descriptive analysis to determine the magnitude of patient engagement in health care decision-making. Multivariate logistic regression analysis was used to determine factors associated with patients' engagement in the health care decision-making process. Adjusted odds ratio with a 95% confidence interval was calculated to measure the strength of association. We declared statistical significance at p<0.05. we presented the results in tables and graphs. 406 patients with chronic diseases took part in the study, yielding a response rate of 96.2%. Less than a fifth [19.5% (95% CI: 15.5, 23.6)] of participants in the study area had a high engagement in their health care decision-making. Educational level (college or above) [AOR = 5.2, 95% CI (1.76-15.46)], duration of diagnosis >5 years [AOR = 1.8, 95% CI (1.03-3.2)], health literacy [AOR = 1.15, 95% CI (1.06-1.24)], autonomy preference in decision making [AOR = 1.35, 95% CI (1.03-1.96)] were factors significantly associated with participants' engagement in health care decision making among patients with chronic diseases. A high number of respondents had a low engagement in their health care decision-making. Preference for autonomy in decision making, educational level, health literacy, duration of diagnosis with the disease were factors associated with patient engagement in health care decision making among patients with chronic diseases in the study area. Thus, patients should be empowered to be involved in decision making to increase their engagement in the care.

Rice irrigation water efficiency improvement: An AquaCrop-based optimization modeling approach

Rice is the cereal crop that consumes the largest amount of water in the world. To improve the water use efficiency of rice farming and promote the sustainable development of rice production, this study proposes a novel AquaCrop-based optimization modeling approach. First, an AquaCrop-based multiobjective model is constructed to generate dynamic water production functions (WPFs). The NSGA-II algorithm is employed to solve the model, and quadratic dynamic WPFs for rice under different hydrological years (wet, normal and dry) are generated based on the obtained Pareto fronts, which can improve the modeling efficiency and help reflect the complicated reality of water flows. Second, a fuzzy credibility-constrained stochastic multiobjective programming (FCC-SMOP) model is developed for irrigation water allocation based on the acquired WPFs. The model addresses the randomness of precipitation and runoff, the fuzziness of decision-makers' preferences, and conflicting objectives of water savings, benefit increments, and water demand during the critical period of rice growth. The proposed simulation-optimization approach was applied to the Changgang irrigation district in northeast China. Optimal irrigation amounts under different decision-making preference scenarios (including conservative, moderate and positive) and different hydrological years were simultaneously generated. The following conclusions can be drawn: (1) The AquaCrop-based multiobjective method is capable of generating WPFs of different hydrological years with sparse discontinuous field experiments; (2) the decision-making preferences of decision-makers significantly impact the economic benefits, water use efficiency, and practicality of schemes; and (3) the FCC-SMOP model successfully makes tradeoffs among the three objectives under multiple uncertainties and can further improve the system efficiency and increase planting income in the Changgang irrigation district when its irrigation facilities become optimal.

Value-based neural representations predict social decision preferences.

Social decision-making is omnipresent in everyday life, carrying the potential for both positive and negative consequences for the decision-maker and those closest to them. While evidence suggests that decision-makers use value-based heuristics to guide choice behavior, very little is known about how decision-makers' representations of other agents influence social choice behavior. We used multivariate pattern expression analyses on fMRI data to understand how value-based processes shape neural representations of those affected by one's social decisions and whether value-based encoding is associated with social decision preferences. We found that stronger value-based encoding of a given close other (e.g. parent) relative to a second close other (e.g. friend) was associated with a greater propensity to favor the former during subsequent social decision-making. These results are the first to our knowledge to explicitly show that value-based processes affect decision behavior via representations of close others.

Multi-objective two-stage stochastic unit commitment model for wind-integrated power systems: A compromise programming approach

In this paper, we introduce a compromise programming (CP) framework for solving a multi-objective two-stage stochastic unit commitment problem characterized by high penetration of wind power. The proposed framework aims at finding best-compromise Pareto efficient on/off schedules, accounting for wind and power demand uncertainties: such solutions must trade off the three objectives of operating cost, CO2 emissions, and wind power curtailment in accordance to the decision maker preferences. To achieve this, we introduce a practical procedure to compute the ideal and Nadir points associated to the multi-objective two-stage stochastic unit commitment problem and propose a linearized ℓ1 norm-based compromise program to design best-compromise on/off schedules that correspondingly minimize and maximize their weighted distances to the ideal point and to the Nadir point, considering the preference weights assigned by the decision maker to each of the three objective functions. The proposed CP framework is applied to a case study related to the New England IEEE-39 bus test system. The results show that, compared to the schedule obtained through the traditional minimization of operating cost, the designed best-compromised schedules considerably improve CO2 emissions reduction and wind power curtailment performance by conservatively sacrificing operating cost performance.

Weighted OWA (Ordered Weighted Averaging Operator) Preference Aggregation for Group Multicriteria Decisions

Group decision making is an integral part of operations and management functions in almost every business domain with substantial applications in finance and economics. In parallel to human decision makers, software agents operate in business systems and environments, collaborate, compete and perform algorithmic decision-making tasks as well. In both settings, information aggregation of decision problem parameters and agent preferences is a necessary step to generate group decision outcome. Although plenty aggregation information approaches exist, overcomplexity of the underlying aggregating operation, in most of them, is a drawback, especially for human based group decisions in practice. In this work we introduce an aggregation method for group decision setting, based on the Weighted Ordered Averaging Operator (WOWA). The aggregation is applied on decision maker preferences, following the majority concept to generate a unique set of preferences as input for the decision algorithm. We present the theoretical construction of the model and an application at a group multicriteria assignment decision problem, along with detailed numerical results. The proposed method contributes in the field, as it offers a novel approach that is simple and intuitive, and avoids overcomplexity during group decision process. The method can be also easily deployed into artificial environments and algorithmic decision-making mechanisms.