Design Choices Research Articles

Methods, techniques and practices for research on poor consumers: A state of the art and a vade-mecum

Marketing research targeting poor consumers has been developing recently. The specific characteristics of these consumers make it difficult to conduct empirical research. This difficulty has raised the question of how to adapt the methods and techniques usually used in marketing. This article provides a multidisciplinary review of the difficulties encountered in addressing people living in poverty and enables researchers to assess this population and anticipate their needs. In addition, based on a literature review of the methods used in 83 empirical marketing studies published between 1990 and 2021, our study provides researchers with a vade-mecum to guide them in their choice of research design. Concrete and detailed recommendations are provided to facilitate their involvement in the field of poor consumers.

Using EPCG for Designing a Hexagon Tangram Puzzle

Designing and tuning a game is a complex creative process, with a variety of tools have been designed for assisting human designers in this process. But, many existing tools either entirely take the design away from humans, or they only assist in generating content - they cannot be used to create an entire game from scratch. This paper explores how Exhaustive Procedural Content Generation (EPCG) can be integrated as a co-creative partner in the design process. We describe how EPCG was used alongside human designers to create a novel Hexagon Tangram puzzle, from initial conception to puzzle curriculum. EPCG is used to answer design questions at each step of the design, which enable human designers to make more informed design choices.

Gas fees on the Ethereum blockchain: from foundations to derivative valuations

The “gas fee” paid for inclusion in the blockchain is analyzed in two parts. First, we consider how “effort” in terms of resources required to process and store a transaction turns into a “gas limit,” which, through a fee comprised of the “base” and “priority fee” in the current version of Ethereum, is converted into the cost paid by the user. We adhere closely to the Ethereum protocol to simplify the analysis and to constrain the design choices when considering “multidimensional gas.” Second, we assume that the “gas” price is given deus ex machina by a fractional Ornstein–Uhlenbeck process and evaluate various derivatives. These contracts can, for example, mitigate gas cost volatility. The ability to price and trade “forwards” in addition to the existing “spot” inclusion into the blockchain could enable users to hedge against future cost fluctuations. Overall, this article offers a comprehensive analysis of gas fee dynamics on the Ethereum blockchain, integrating supply-side constraints with demand-side modelling to enhance the predictability and stability of transaction costs.

A path integral approach for allele frequency dynamics under polygenic selection.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change. Predicting allele frequency changes under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Can participatory democracy become “inclusive”? Class, mobilization and voice in participatory institutions

Abstract This article discusses reproductionist perspectives that assume there is little local participatory institutions can do to address the underrepresentation and the domination of some social groups. While there is also empirical basis to be skeptical, the evidence suggests that, occasionally, the reproduction of class inequalities can be counteracted. This encourages us to consider the conditions that favor greater participation of working-class, economically and culturally disadvantaged people. Comparing evidence from various studies in a range of countries, the article argues that certain contextual factors and inclusion tools produce higher rates of mobilization and more egalitarian deliberations. Specifically, the article focuses on the effects of three conditions: a) special mobilization efforts; b) design choices and inclusion tools; and c) the broadening of the political subject through cultural mobilization. As well as reflecting on the shortcomings of these factors, a new research agenda for social equality in participation is also proposed.

Secondary science teachers' conceptualizations and modifications to support equitable participation in a co‐designed computational thinking lesson

AbstractIncreasing access to computational ideas and practices is one important reason to integrate computational thinking (CT) in science classrooms. While integrating CT into science classrooms broadens exposure to computing, it may not be enough to ensure equitable participation in the science classroom. Equitable participation is crucial because providing students with an environment in which they are able to fully engage and participate in science and computing practices empowers students to learn and continue pursuing CT and science. To foreground equitable participation in CT‐integrated curricula, we undertook a research project in which researchers and teachers examined teacher conceptualizations of equitable participation and how teachers design for equitable participation by modifying a lesson that introduces computational modeling in science. The following research questions guided the study: (1) What are teachers' conceptualizations of equitable participation? (2) How do teachers design for equitable participation through co‐design of a CT‐integrated unit? Our findings suggest that teachers conceptualized and designed for equitable participation in the context of a CT‐integrated curriculum across three primary dimensions: accessibility, inclusion, and relevancy. Our contributions to the field of science teaching and learning are twofold: (1) obtaining an initial understanding of how teachers think about and design for equitable participation is crucial in order to support teachers in their pursuit of creating equitable learning experiences for CT and science learners, and (2) our findings show that we can study teacher conceptualizations and their design choices by examining specific modifications to a CT‐integrated science curriculum. Implications are discussed.

A randomized controlled trial on the nomenclature of scientific computing

ABSTRACT Background and Context Data science and statistics are used across a broad spectrum of professions, experience levels and programming languages. The popular scientific computing languages, such as Matlab, Python and R, were organized without using empirical methods to show evidence for or against their design choices, resulting in them feeling eclectic or esoteric in their design. Objective To meaningfully organize scientific computing based on evidence gathered through user feedback, build a statistical package based on the findings and provide a replication packet to run similar studies on people with different backgrounds. Method A randomized controlled trial using a weighted, ranked choice survey (n = 118) with between-subjects design having two independent variables: Language Group (Matlab, Python and R) and Method Name options. Our dependent variable was a normalized preference rating. Findings There was a very small interaction between Language Group and Method Name. Language Group did not have a statistically significant effect, but Method Name did (F(4, 27037) = 2211.23, p < .001)( η p 2 = .247). Finally, many names in Matlab, Python and R were ranked so poorly that they were not statistically significantly different from a random word in 63.0%, 62.2% and 30.4% of concepts respectively. Implications We found organized and structured names were ranked by a large margin, suggesting statistical programming today likely needs considerable improvement. Finally, we outline a statistical package built using these principles, provide comparison scripts and describe some of the challenges from going from simple surveys to in-practice libraries.

Scabbard: An Exploratory Study on Hardware Aware Design Choices of Learning with Rounding-based Key Encapsulation Mechanisms

Recently, the construction of cryptographic schemes based on hard lattice problems has gained immense popularity. Apart from being quantum resistant, lattice-based cryptography allows a wide range of variations in the underlying hard problem. As cryptographic schemes can work in different environments under different operational constraints such as memory footprint, silicon area, efficiency, power requirement, and so on, such variations in the underlying hard problem are very useful for designers to construct different cryptographic schemes. In this work, we explore various design choices of lattice-based cryptography and their impact on performance in the real world. In particular, we propose a suite of key-encapsulation mechanisms based on the learning with rounding problem with a focus on improving different performance aspects of lattice-based cryptography. Our suite consists of three schemes. Our first scheme is Florete, which is designed for efficiency. The second scheme is Espada, which is aimed at improving parallelization, flexibility, and memory footprint. The last scheme is Sable, which can be considered an improved version in terms of key sizes and parameters of the Saber key-encapsulation mechanism, one of the finalists in the National Institute of Standards and Technology’s post-quantum standardization procedure. In this work, we have described our design rationale behind each scheme. Furthermore, to demonstrate the justification of our design decisions, we have provided software and hardware implementations. Our results show Florete is faster than most state-of-the-art KEMs on software platforms. For example, the key-generation algorithm of high-security version Florete outperforms the National Institute of Standards and Technology’s standard Kyber by 47%, the Federal Office for Information Security’s standard Frodo by 99%, and Saber by 57% on the ARM Cortex-M4 platform. Similarly, in hardware, Florete outperforms Frodo and NTRU Prime for all KEM operations. The scheme Espada requires less memory and area than the implementation of most state-of-the-art schemes. For example, the encapsulation algorithm of high-security version Espada uses 30% less stack memory than Kyber, 57% less stack memory than Frodo, and 67% less stack memory than Saber on the ARM Cortex-M4 platform. The implementations of Sable maintain a tradeoff between Florete and Espada regarding software performance and memory requirements. Sable outperforms Saber at least by 6% and Frodo by 99%. Through an efficient polynomial multiplier design, which exploits the small secret size, Sable outperforms most state-of-the-art KEMs, including Saber, Frodo, and NTRU Prime. The implementations of Sable that use number theoretic transform-based polynomial multiplication (SableNTT) surpass all the state-of-the-art schemes in performance, which are optimized for speed on the Cortext M4 platform. The performance benefit of SableNTT against Kyber lies in between 7-29%, 2-13% for Saber, and around 99% for Frodo.

Innovations and Insights in Superhydrophobicity: Learning from Nature to Surpass It

Abstract. Superhydrophobic surfaces, inspired by natural examples, have emerged as a promising area of research due to their unique properties. This paper reviews the characteristics and challenges of creating surfaces that can exceed the capabilities of natural counterparts. Key aspects include surface tension, contact angle, and morphology, with a focus on natural models such as lotus leaves and gecko feet. The paper highlights knowledge gaps in durability and manufacturing, and discusses design challenges in achieving roughness, hierarchy, and balance between hydrophobicity and durability. Methods for enhancing durability, such as chemical and roughness restoration, are explored, alongside the potential of AI in optimizing material and design choices. The paper concludes with recommendations for future research directions in the field.

Toward Robust Car-Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations

The selection of the target variable is critical for the performance of classical car-following (CF) models. There is a vast body of literature on which target variable is optimal for classical CF models, but there is no study that evaluates the selection of optimal target variables for black-box models prevalent in machine learning, which are increasingly being used to model CF behavior. This paper evaluates different target variables, for example acceleration, velocity, and headway, for three models: Gaussian processes (GP), kernel ridge regression, and long short-term memory (LSTM). These models have different objective functions and work in different vector spaces, for example, GP work in function space, and LSTM in parameter space. Furthermore, this paper evaluates the efficacy of black box and classical CF models across datasets, including diverse traffic scenarios and human versus automated control. Finally, this paper evaluates the coupling of design choices across selection of model type, target variable during model calibration, and dataset characteristics. The experiments show that the optimal target variable recommendations for black-box models differ from classical CF models, depending on the objective function and the vector space. Statistical analysis indicates that selection of models couples greatly with target variables, while recommended target variables do not depend on the datasets used during model calibration. These results provide a path toward more robust CF models, potentially resulting in more accurate and generalizable capacity and/or traffic flow estimates for roadways, even as driving behaviors change with the introduction of new types of automation.

Investigating Nudges toward Related Sellers on E-commerce Marketplaces: A Case Study on Amazon

E-commerce marketplaces provide business opportunities to millions of sellers worldwide. Some of these sellers have special relationships with the marketplace by virtue of using their subsidiary services (e.g., fulfillment and/or shipping services provided by the marketplace) -- we refer to such sellers collectively as Related Sellers. When multiple sellers offer to sell the same product, the marketplace helps a customer in selecting an offer (by a seller) through (a) a default offer selection algorithm, (b) showing features about each of the offers and the corresponding sellers (price, seller performance metrics, seller's number of ratings etc.), and (c) finally evaluating the sellers along these features. In this paper, we perform an end-to-end investigation into how the above apparatus can nudge customers toward the Related Sellers on Amazon's four different marketplaces in India, USA, Germany and France. We find that given explicit choices, customers' preferred offers and algorithmically selected offers can be significantly different. We highlight that Amazon is adopting different performance metric evaluation policies for different sellers, potentially benefiting Related Sellers. For instance, such policies result in notable discrepancy between the actual performance metric and the presented performance metric of Related Sellers. We further observe that among the seller-centric features visible to customers, sellers' number of ratings influences their decisions the most, yet it may not reflect the true quality of service by the seller, rather reflecting the scale at which the seller operates, thereby implicitly steering customers toward larger Related Sellers. Moreover, when customers are shown the rectified metrics for the different sellers, their preference toward Related Sellers is almost halved. We believe our findings will inform and encourage further deliberation toward more effective governance of such design choices and policies adopted by e-commerce marketplaces.

A multimodal understanding of the role of sound and music in gendered toy marketing.

Literature in music theory and psychology shows that, even in isolation, musical sounds can reliably encode gender-loaded messages. Musical material can be imbued with many ideological dimensions and gender is just one of them. Nonetheless, studies of the gendering of music within multimodal communicative events are sparse and lack an encompassing theoretical framework. The present study attempts to address this literature gap by employing a critical quantitative analysis of music in gendered toy marketing, which integrated a content analytical approach with multimodal affective and music-focused perceptual responses. Ratings were collected on a set of 606 commercials spanning a ten-year time frame and strong gender polarization was observed in nearly all of the collected variables. Gendered music styles in toy commercials exhibit synergistic design choices, as music in masculine-targeted adverts was substantially more abrasive-louder, more inharmonious, and more distorted-than in feminine-targeted ones. Thus, toy advertising music appeared deliberately and consistently in line with traditional gender norms. In addition, music perceptual scales and voice-related content analytical variables explain quite well the heavily polarized affective ratings. This study presents a empirical understanding of the gendering of music as constructed within multimodal discourse, reiterating the importance of the sociocultural underpinnings of music cognition. We provided a public repository with all code and data necessary to reproduce the results of this study on github.com/marinelliluca/music-role-gender-marketing.

What Type of Self-Assessment Is Best for Your Educational Activity? A Review of Pre-Post, Now-Then, and Post-Only Designs.

In medical education, learner self-assessments are standard methods used to evaluate the impact of curricula and workshops. Due to the subjective nature of self-assessment, these measures are prone to known biases including framing, recall, social desirability, and response-shift bias. These biases can contribute to floor and ceiling effects of measurement, which can lead to false conclusions about whether the intended learning objectives were achieved. Ideal assessments of skills-based educational activities would include standardized tests and structured observations of learners demonstrating skill use before and after the educational intervention. However, educators often lack the necessary resources, time, and expertise to routinely conduct these appraisals and rely on self-assessment as a pragmatic approach to obtaining curriculum feedback and evaluation data. In this review, we describe three common designs for self-assessments: the pre-post, now-then, and post-only designs. We then give recommendations for choosing between each design to minimize bias. The choice of the best design is based on alignments with four considerations: (1) the educational objectives (e.g., demonstrate skill competency and/or change in skill level); (2) participants' prior experience and shared understanding of levels of skill performance; (3) the nature of the educational activity; and (4) available resources. For each design, we review strengths, weaknesses, and known biases and discuss examples to highlight trade-offs between options. We also discuss the use of control groups and follow-up surveys to measure retention over time as additional methods to address bias and related confounding. The guidance presented here is intended to raise educators' awareness of common pitfalls in self-assessment; minimize the impact of known biases when possible; provide evidence, examples, and rationales for optimal design choices; and increase the rigor of self-assessment evaluations.

A Multicriteria Decision Analytic Approach to Systems Resilience

AbstractThis article develops a novel decision-oriented framework that strategically deconstructs systems resilience in a way that focuses on systems’ design, capabilities, and management. The framework helps evaluate and compare how system design choices impact system resilience. First, we propose a resilience score based on a piecewise linear approximation to a resilience curve. Using multicriteria decision analysis principles, we score system design alternatives in terms of system-specific capabilities. We estimate the relevance of these capabilities to resilience curve parameters associated with resilience phases. Finally, we interpret the derivatives of resilience with respect to the curve parameter values as the leverage of these parameters. Using multiple levels of weighted sums of the scores, we calculate the first order impact of system design choices first on a proxy for the generic resilience parameters and then on resilience, which allows situational characteristics to be incorporated in their natural terminology while mapping their impact on resilience with a traceable logic. We illustrate the approach by using existing materials to develop an example comparing engineered designs for minimizing post-wildfire flood impacts.

A simulation-based bias analysis to assess the impact of unmeasured confounding when designing nonrandomized database studies.

Unmeasured confounding is often raised as a source of potential bias during the design of nonrandomized studies, but quantifying such concerns is challenging. We developed a simulation-based approach to assess the potential impact of unmeasured confounding during the study design stage. The approach involved generation of hypothetical individual-level cohorts using realistic parameters, including a binary treatment (prevalence 25%), a time-to-event outcome (incidence 5%), 13 measured covariates, a binary unmeasured confounder (u1; 10%), and a binary measured "proxy" variable (p1) correlated with u1. Strengths of unmeasured confounding and correlations between u1 and p1 were varied in simulation scenarios. Treatment effects were estimated with (1) no adjustment, (2) adjustment for measured confounders (level 1), and (3) adjustment for measured confounders and their proxy (level 2). We computed absolute standardized mean differences in u1 and p1 and relative bias with each level of adjustment. Across all scenarios, level 2 adjustment led to improvement in the balance of u1, but this improvement was highly dependent on the correlation between u1 and p1. Level 2 adjustments also had lower relative bias than level 1 adjustments (in strong u1 scenarios: relative bias of 9.2%, 12.2%, and 13.5% at correlations of 0.7, 0.5, and 0.3, respectively, vs 16.4%, 15.8%, and 15.0% for level 1). An approach using simulated individual-level data is useful to explicitly convey the potential for bias due to unmeasured confounding while designing nonrandomized studies, and can be helpful in informing design choices. This article is part of a Special Collection on Pharmacoepidemiology.

Representation and Its Historical Antecedents in New Media: The Queer-ious Case of Assassin's Creed

Videogames, like other new media, reflect and shape culture and are influenced by societal attitude shifts. We conducted a qualitative close-reading analysis of queer representation in the AAA historical-fiction series Assassin's Creed and the historical antecedents thereof to explore (a) the traditional marginalization of queer-identifying folks in (b) the context of growing societal acceptance within the (inter)national zeitgeist. In our examination of the developer's reaction operationalized as observable shifts in developers’ design choices, we observed an increase in representation that aligns with the shift in social attitudes toward queer folks over the last 15 years. With respect to the historical antecedents, we discovered a misalignment between the in-game portrayal of queer folks and acceptance thereof vis a vis the available historical record.

Site Reassignment for Mobile Outreach Teams: Investigating the Effectiveness of Decentralized Decision Making

Problem definition: United Nations Sustainable Development Goal (SDG) 3.8 states that health coverage should be universal by 2030. This is challenging in rural and poor areas. To address this challenge, mobile outreach teams of healthcare workers visit a fixed set of remote sites to provide healthcare services. Because of dynamics in demand and supply, once-rational site-to-team assignment decisions can become far from optimal over time. This paper considers the problem of reassigning sites to teams to maximize effectiveness. Solving this problem through a central planner does not fit the context: outreach teams commonly have a high degree of decision-making autonomy. We study a decentralized approach where subsets of teams collaborate in a series of team meetings to reassign sites. The key question for decision makers is whether and when such an approach is effective. Methodology/results: We propose a mixed-integer programming model for centralized site reassignment. We extend this model to represent the decentralized approach and develop a set of simple decision rules for this approach. We use empirical data from six country outreach programs of the nongovernmental organization MSI Reproductive Choices. Our results suggest that, when properly designed, decentralized decision making performs close to centralized decision making, and even outperforms it in the case of inaccurate information at the central level. The finding remains valid when demand and supply fluctuate, and is insensitive to the chosen objective. Managerial implications: Humanitarian organizations currently deploy mobile outreach teams to provide a wide variety of health services. We present several useful insights for decision makers in humanitarian organizations when making design choices, taking account of context. In particular, we show that decentralized site reassignment provides a feasible and effective alternative to centralized approaches in many contexts. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0437 .

Keep It Accurate and Robust: An Enhanced Nuclei Analysis Framework

Accurate segmentation and classification of nuclei in histology images is critical but challenging due to nuclei heterogeneity, staining variations, and tissue complexity. Existing methods often struggle with limited dataset variability, with patches extracted from similar whole slide images (WSI), making models prone to falling into local optima. Here we propose a new framework to address this limitation and enable robust nuclear analysis. Our method leverages dual-level ensemble modeling to overcome issues stemming from limited dataset variation. Intra-ensembling applies diverse transformations to individual samples, while inter-ensembling combines networks of different scales. We also introduce enhancements to the HoVer-Net architecture, including updated encoders, nested dense decoding and model regularization strategy. We achieve state-of-the-art results on public benchmarks, including 1st place for nuclear composition prediction and 3rd place for segmentation/classification in the 2022 Colon Nuclei Identification and Counting (CoNIC) Challenge. This success validates our approach for accurate histological nuclei analysis. Extensive experiments and ablation studies provide insights into optimal network design choices and training techniques. In conclusion, this work proposes an improved framework advancing the state-of-the-art in nuclei analysis. We will release our code and models to serve as a toolkit for the community.

Explaining the impact of design choices on model quality in predictive process monitoring

Explaining the impact of design choices on model quality in predictive process monitoring

The integration of Artificial Intelligence (AI) into undergraduate education

The incorporation of AI as a tool in educating undergraduates is a promising concept held with a great potential of positively influencing the learning process, learners’ experiences and institutional management systems. At the same time, it lifts essential moral issues that ought to be adequately addressed into new stages. The aim of this paper is to review literature on the subject of ethics in the application of AI in the context of an undergraduate degree program. Algorithmic choices for instructional design are another relevant ethical concern along with privacy and data protection, practitioners’ responsibilities, and the requisite levels of transparency, as well as the depersonalized and mechanized nature of learning management systems. The analysis reveals how the use of AI technology might deepen the social and economic inequities the students have described and how difficult it is to keep the students’ trust in cases when AI technology is not transparent. Also, the paper raises concerns related to several ethical issues and their consequences to students, teachers, and institutions on how strong ethical principles and policies to apply AI in the educational sectors. It is established that the use of AI in the delivery of undergraduate education has the potential of enhancing the effectiveness of learning(). Nonetheless, it is recommended that the integration of AI in the classroom must be done with reference to several ethical concerns so as to prevent further negative ramifications of the technology.