Archaeological sampling is a critical yet inconsistently applied aspect of field methodology. Poorly designed strategies produce biased or irreproducible results, especially when recovery is labor-intensive and research expectations are high. This article addresses that challenge through the lens of spatial microrefuse analysis, using simulation modeling to evaluate current practices and improve sampling design, training, and planning. A review of 27 published microrefuse studies reveals wide variation in sampling strategies, unit sizes, and volumes, with little evidence of statistical justification. To explore the consequences of this variation, I introduce the Archaeological Sampling Experiment Laboratory (tASEL), an open-source simulation tool developed in NetLogo and archived in the CoMSES model library. tASEL allows archaeologists to construct artifact distributions and test random, systematic, or hybrid sampling frames with immediate visual and statistical feedback. I used tASEL to conduct 22,000 virtual sampling experiments across two artifact distributions: a diffuse random scatter and a highly clustered pattern. Results show that sampling performance varies significantly by distribution, sample size, and frame design. Random strategies produced the highest accuracy and lowest bias. I conclude by demonstrating how tASEL can be used in classroom and field contexts to improve sampling literacy and support more robust archaeological practice.

Fractional Brownian motion, with its long-time correlated increments, has been applied in many fields in recent years. Since volatility was shown to be rough by Gatheral, Jaisson, and Rosenbaum, fractional Brownian motion has gained popularity as a financial model. In this work, we revisit the definitions and properties of the univariate and multivariate fractional Brownian motions, and consider four simulation methods. We demonstrate the issues associated with applying the standard Euler scheme for simulating stochastic processes driven by fractional Brownian motion with $H < \frac{1}{2}$ (which we call the rough models). We then introduce a novel approximate method for simulating such rough models based on the fast algorithm by Ma and Wu, which accounts for a factor of 10 speedup. Finally, we consider applications of these methods to option pricing.

Extropy-based divergence measures offer distinct advantages over entropy-based counterparts, owing to their mathematical simplicity and enhanced interpretability. Relative extropy by Lad et al. [5] is a symmetric divergence measure between two probability distributions, and Mohammadi et al. [8] introduced the asymmetric divergence between two distributions based on extropy. We further study these measures, their properties, and interrelationships in this article. To address the divergence between truncated lifetime distributions, we define dynamic relative extropy for residual and past lifetime scenarios. Exploring the interrelationships of dynamic cases of relative extropy, extropy divergence, and extropy inaccuracy, we derive some unique properties and characterizations for the exponential distribution. A nonparametric estimator for relative extropy is developed, and its performance is assessed through numerical simulation studies. The practical applicability of relative extropy is used to analyze the divergence in lifetime patterns of mice under a lifetime feeding experiment and the shopping patterns of customers based on age and income groups. Further, the application of relative extropy is also applied to find the dissimilarity between two images.

Understanding the causes and drivers of extinction is critical for mitigating future anthropogenic extinctions. This study explored the extinction process of the Crested Ibis Nipponia nippon in Japan, focusing on the last wild population on Sado Island. An integrated population model–population viability analysis (IPM-PVA) framework was applied to estimate demographic parameters and population dynamics using four historical data sources, i.e. population counts, records of dead or rescued individuals, reproductive data, and captures for captive breeding. The IPM estimated an average of 0.703 fledglings per breeding pair per season, with adult and juvenile survival rates of 0.870 and 0.730, respectively. Human disturbances were found to substantially reduce fecundity. PVA results indicated an extinction probability of 56.6% under observed historical conditions, which could have been reduced to 11.2% if human access to nesting forests had been restricted. The study identified low fecundity caused by human disturbance at nest-sites as a likely contributor to the species’ extinction. Despite the need for cautious interpretation due to data limitations, this study highlights the practical utility of the IPM-PVA framework in providing detailed insights into the extinction process.

Word frequency databases like SPALEX and SUBTLEX-ESP treat Spanish as a uniform language, but prior studies and an initial survey (Experiment 1) revealed significant lexical differences between Spanish in Spain and Latin American countries, especially Chile. To establish subjective frequencies of Spanish word usage, an extended survey (Experiment 2) was conducted with Chilean participants, categorizing words by usage area: General, Spain, Chile, and Latin America. Consistent with the initial survey, Chilean participants assigned subjective higher ratings to General and Chilean words. In a lexical decision experiment (Experiment 3), participants responded faster and more accurately to words from these categories. Using survey data, simulations with Multilink+ (Experiment 4) revealed that subjective word ratings better predicted Chilean reaction times than frequencies from existing databases. These findings emphasize the need to address Spanish dialectal differences in research, with word ratings offering a more accurate measure of region-specific lexical nuances than current databases.

Random-effects meta-analysis is a widely applied methodology to synthesize research findings of studies related to a specific scientific question. Besides estimating the mean effect, an important aim of the meta-analysis is to summarize the heterogeneity, that is, the variation in the underlying effects caused by the differences in study circumstances. The prediction interval is frequently used for this purpose: a 95% prediction interval contains the true effect of a similar new study in 95% of the cases when it is constructed, or in other words, it covers 95% of the true effects distribution on average in repeated sampling. In this article, after providing a clear mathematical background, we present an extensive simulation investigating the performance of all frequentist prediction interval methods published to date. The work focuses on the distribution of the coverage probabilities and how these distributions change depending on the amount of heterogeneity and the number of involved studies. Although the single requirement that a prediction interval has to fulfill is to keep a nominal coverage probability on average, we demonstrate why the distribution of coverages should not be disregarded. We show that for meta-analyses with small number of studies, this distribution has an unideal, asymmetric shape. We argue that assessing only the mean coverage can easily lead to misunderstanding and misinterpretation. The length of the intervals and the robustness of the methods concerning the non-normality of the true effects are also investigated.

Increasingly, simulation-based teaching and learning is finding a place within politics and international relations (IR) programmes. The majority of literature on this style of teaching and learning has positioned it as both an aid to content delivery and as a response to the many challenges facing contemporary higher education. Little guidance is given, however, to the practical considerations of using simulations as a component of assessment or as informing assessed tasks. This article draws upon the experience of the authors in adapting the well-established Model United Nations (MUN) simulation programme for delivery as an assessed module at a British university. This has involved balancing institutional teaching, assessment and validation requirements with the successful simulation of diplomatic practice. The article introduces the MUN simulation and explores the extant pedagogic literature encouraging the use of simulation-based learning in IR curricula, before moving on to provide an overview of the rationale for the various decisions the authors have made in adapting the simulation for delivery as an assessed curriculum component. The article asserts the value of introducing assessed simulations within IR coursework and provides guidance on how student performance in pedagogic simulations might best be assessed.

Collaboration and its promotion by funders continue to accelerate. Although research has identified significant transaction costs associated with collaboration, little empirical work has examined the broader, societal-level economic outcomes of a resource-sharing environment. Does an environment that encourages collaboration shift our focus toward certain types of social objectives and away from others? This paper uses agent-based Monte Carlo simulation to demonstrate that collaboration is particularly useful when resources are rare but a social objective is commonly held. However, collaboration can lead to bad outcomes when the objective is not commonly shared; in such cases, markets outperform collaborative arrangements. These findings suggest that encouraging a resource-sharing environment can lead to inefficiencies even worse than market failure. We also demonstrate that failure to account for transaction costs when prescribing collaboration can result in quantifiably lower outcome levels than expected.

In recent years, a growing body of literature has widely investigated the impact of role-playing simulations in teaching politics and international relations. While scholars agree that participating in simulations is helpful for the students in developing their skills, the evidence about benefits is more mixed. Moreover, the question whether all students—regardless of their demographic or academic background—benefit similarly from simulations remains largely unanswered. This article, based on a cross-national survey submitted to students from Italy and the Netherlands who have participated in the Model United Nations (MUN), provides an innovative contribution to the current literature by looking at views and opinions of students coming from different educational contexts. Our empirical results suggest that students perceive that MUN increases their skills regardless of their academic and socio-demographic background. The quantitative analysis, based on OLS regression models, reveals that the individual students’ background does not influence their perceived benefit, nor their enjoyment of the experience. MUNs appear to be educational as well as fun for all students, regardless of their age, gender, field of study, seniority, and academic homeland.

Recent literature suggests that undergraduate students in political science dread studying methods, especially quantitative methods. Nonetheless, we believe that teaching students the value of quantitative analysis is critical. Going beyond traditional teaching approaches, we use a simulation to conceptualize the logic and process of quantitative analysis. In our simulation, student participants are told they are in a prison with another prisoner, where both have the objective to make the other prisoner crossover, using any strategy. The simulation allows students to generate and test basic hypothesis about students’ characteristics and their strategies, as well as operationalizing variables and quantifying results.

The benefits of simulation exercises easily outweigh potential weaknesses, and most of these weaknesses can be addressed by careful preparation. This article seeks to encourage instructors in higher education to embrace simulations as a means of encouraging active learning and greater retention as well as improving student and teacher satisfaction. However, there is not to date much helpful guidance, for first-time appliers, as to how to set up simulations. This contribution seeks to contribute to closing that gap by reflecting on the experience of two EU Council simulations that the author has organised. The aim is to openly review things that worked well and things that did not so as to allow colleagues interested in engaging in simulations in the future to see the reasons behind certain choices and perhaps avoid weaknesses of simulations set up by ‘freshers’. In this context, articles are all too often presented as success stories, hiding errors or adaptations in the process, whereas in fact much can be learned from publically exposing and reflecting upon shortcomings and weaknesses of research and teaching design and processes. To finish up, some tips for ‘freshers’ have been compiled.