As the field of heritage language acquisition expands, there is a need for proficiency to compare speakers across groups and studies. Elicited imitation tasks (EITs) are efficient cost-effective tasks with a long tradition in proficiency assessment of second language (L2) learners, first language children, and adults. However, little research has investigated their use with heritage speakers (HSs), despite their oral nature, which makes them appropriate for speakers with variable literacy skills. This study is a partial replication of Solon, Park, Dehghan-Chaleshtori, Carver & Long (2022), who administered an EIT originally developed for advanced L2 learners on a group of HSs. In this study, we administered the same EIT with minor modifications to 70 HSs and 132 L2 learners of Spanish with different levels of proficiency and ran a Rasch analysis to evaluate the functioning of the task with the two groups. To obtain concurrent validity evidence, scores on the EIT were compared with participants’ performance in an oral narration; evaluated for complexity, accuracy, and fluency (CAF); and compared with a standardized oral proficiency test, the Versant Spanish Test. Results of Rasch analyses showed that the EIT was effective at distinguishing different levels of ability for both groups, and analyses showed moderate to strong correlations between CAF measures and the EIT and very strong correlations between the EIT and the Versant Spanish Test. These results provide evidence that the EIT is an efficient and adequate proficiency test for HSs and L2 learners of Spanish; its use in research settings is recommended.

When aiming to change behavior, policymakers confront the challenge of implementing behavioral interventions across contexts. However, the effectiveness of behavioral solutions often hinges on context, posing a significant hurdle to scaling interventions. This study explores the application of a behavioral pattern language approach as a means to enhance intervention efficacy and support policymakers and practitioners who seek to solve problems at scales that cross diverse contexts. The study demonstrates how a pattern language can inform contextually aware solutions, fostering collaboration and knowledge sharing among stakeholders. Additionally, the research finds practitioners deploy multiple solutions within complex systems to achieve more difficult behavioral change goals. Despite challenges related to replicability and evolving methodologies, the findings suggest that pattern languages offer a promising avenue for systematically generating and disseminating behavioral insights. This research contributes to advancing applied behavioral science by providing a structured approach for collaborative policymaking and research endeavors that are contextually relevant and effective.

Using a laboratory experiment, we investigate complexity in decision problems as a cause of failures in contingent reasoning. For this purpose, we introduce three dimensions of complexity to a decision problem: the number of contingencies, the dominance property of choices, and reducible states. Each decision problem is designed to reflect variations in complexity across the three dimensions. Experimental results show that the number of contingencies has the most significant effect on failures in contingent reasoning. The second dimension, the dominance property of choices, also has a statistically significant effect, though the effect size is smaller than in the existing literature. In contrast, the third complexity dimension has no impact; presenting the decision problem in a reduced or reducible form does not change subjects’ performance on contingent reasoning. Additionally, we examine the Power of Certainty and show its existence. This effect is particularly pronounced when the number of contingencies is large.

Systems thinking is deeply rooted in history, as far back as Aristotle. However, it has only relatively recently reemerged as an approach to help us understand and intervene in health and food systems. This is particularly salient, given its impact on environmental and population health. Whilst global food is abundant, many people cannot access affordable, healthy and culturally appropriate food. On the other hand, foods of low nutrient density are widely available. Food systems are complex and require complex thinking and approaches that allow us to consider the influence of multiple factors and how the might system respond to change. In turn, this enables the identification of ‘leverage’ points, where policies or interventions are most likely to have a sustained impact. The Foresight obesity map inspired others to adopt systems approaches to help understand the broader social, economic, environmental determinants of obesity to support intervention/policy development. Evaluation of these requires a consideration of complexity to explore why intervention goals may or may not have been successful and how relationships between components or approaches can be enhanced to support implementation and thereby increase the potential for effectiveness. Overall, approaches to understand, intervene, govern and evaluate food systems must themselves be sufficiently complex, or will ultimately be destroyed by the system it seeks to improve. This review paper aims to introduce readers to the application of systems approaches in research within the context of food systems and health, including its traditional/historical origins.

The Stochastic Becker-DeGroot-Marschak (SBDM) mechanism is a theoretically elegant way of eliciting incentive-compatible beliefs under a variety of risk preferences. However, the mechanism is complex and there is concern that some participants may misunderstand its incentive properties. We use a two-part design to evaluate the relationship between participants’ probabilistic reasoning skills, task complexity, and belief elicitation. We first identify participants whose decision-making is consistent and inconsistent with probabilistic reasoning using a task in which non-Bayesian modes of decision-making lead to violations of stochastic dominance. We then elicit participants’ beliefs in both easy and hard decision problems. Relative to Introspection, there is less variation in belief errors between easy and hard problems in the SBDM mechanism. However, there is a greater difference in belief errors between consistent and inconsistent participants. These results suggest that while the SBDM mechanism encourages individuals to think more carefully about beliefs, it is more sensitive to heterogeneity in probabilistic reasoning. In a follow-up experiment, we also identify participants with high and low fluid intelligence with a Raven task, and high and low proclivities for cognitive effort using an extended Cognitive Reflection Test. Although performance on these tasks strongly predict errors in both the SBDM mechanism and Introspection, there is no significant interaction effect between the elicitation mechanism and either ability or effort. Our results suggest that mechanism complexity is an important consideration when using elicitation mechanisms, and that participants’ probabilistic reasoning is an important consideration when interpreting elicited beliefs.

We examine experimentally how complexity affects decision-making, when individuals choose among different products with varying benefits and costs. We find that complexity in costs leads to choosing a high-benefit product, with high costs and overall lower payoffs. In contrast, when complexity is in the benefits of the product, we cannot reject the hypothesis of random mistakes. We also examine the role of heterogeneous complexity. We find that individuals still (mistakenly) choose the high-benefit but costly product, even if cheaper and simple products are available. Our results suggest that salience is a main driver of choices under different forms of complexity.

We present an experiment where subjects sequentially receive signals about the true state of the world and need to form beliefs about which one is true, with payoffs related to reported beliefs. We attempt to control for risk aversion using the Offerman et al. (Rev Econ Stud 76(4):1461–1489, 2009) technique. Against the baseline of Bayesian updating, we test for belief adjustment underreaction and overreaction and model the decision making process of the agent as a double hurdle model where agents with inferential expectations first decide whether to adjust their beliefs and then, if so, decide by how much. We also test the effects of increased inattention and complexity on belief updating. We find evidence for periods of belief inertia interspersed with belief adjustment. This is due to a combination of random belief adjustment; state-dependent belief adjustment, with many subjects requiring considerable evidence to change their beliefs; and quasi-Bayesian belief adjustment, with aggregate insufficient belief adjustment when a belief change does occur. Inattention, like complexity, makes subjects less likely to adjust their stated beliefs, while inattention additionally discourages full adjustment.

The literature suggests that product and product-service system (PSS) design problems are characteristically different. However, there is limited empirical evidence to suggest that the design cognition specific to the respective design activities is different. This article reports the findings of a comparative study of protocols of conceptual product and PSS designing carried out in a laboratory environment by 28 pairs of experienced product designers from the manufacturing industry. First, differences between product and PSS design problems were theoretically characterized in terms of their respective sources of complexity. Based on these differences, hypotheses concerning differences in the cognitive processes of conceptual product and PSS designing were developed and empirically tested. Results indicate that PSS designing by experienced product designers is more problem-focused while product designing is more solution-focused. PSS designing was found to focus more on the design issue function and the design process formulation. Further, PSS designing was more likely to apply a depth-first search strategy, while product designing was more apt to apply a breadth-first search strategy. Results point towards the need to support the analysis of derived behavior of structure and the application of a breadth-first strategy during PSS designing by product designers.

Inflectional systems vary along multiple dimensions (number of members, size of paradigms, word class, integrative complexity, accidents of history, etc.). This makes it difficult to find significant correlations and causality relations between different properties, as attested systems usually differ in multiple ways at the same time, thus obscuring possible relations between individual variables. Here we analyze the relation between a system’s size by number of members and its morphological complexity. We do so by exploring in detail, via quantitative methods, the cognate inflectional systems of Central Pame and Chichimec (Otomanguean, Mexico), whose inflecting nominal classes differ precisely mostly with regard to their size (i.e. number of members).

Digital twins are a new paradigm for our time, offering the possibility of interconnected virtual representations of the real world. The concept is very versatile and has been adopted by multiple communities of practice, policymakers, researchers, and innovators. A significant part of the digital twin paradigm is about interconnecting digital objects, many of which have previously not been combined. As a result, members of the newly forming digital twin community are often talking at cross-purposes, based on different starting points, assumptions, and cultural practices. These differences are due to the philosophical world-view adopted within specific communities. In this paper, we explore the philosophical context which underpins the digital twin concept. We offer the building blocks for a philosophical framework for digital twins, consisting of 21 principles that are intended to help facilitate their further development. Specifically, we argue that the philosophy of digital twins is fundamentally holistic and emergentist. We further argue that in order to enable emergent behaviors, digital twins should be designed to reconstruct the behavior of a physical twin by “dynamically assembling” multiple digital “components”. We also argue that digital twins naturally include aspects relating to the philosophy of artificial intelligence, including learning and exploitation of knowledge. We discuss the following four questions (i) What is the distinction between a model and a digital twin? (ii) What previously unseen results can we expect from a digital twin? (iii) How can emergent behaviours be predicted? (iv) How can we assess the existence and uniqueness of digital twin outputs?