The model of hierarchical complexity (mhc) is a mathematical model based on the “Theory of Measurement” that has gone through a number of iterations as a measurement system (Commons, Goodheart, Pekker, et al., 2005; Commons & Pekker, 2008; Commons & Richards, 1984a, 1984b; Commons, Trudeau, Stein, et all, 1998). It sets forth the measurement system by which actions are put into a hierarchical order and each order is assigned an ordinal number. In this paper, the components of the model will be described: actions and tasks, measurement and I operations, and the axioms, followed by an articulation of emerging properties from axioms, and then a description I of orders of hierarchical complexity of tasks. These are a reworked smaller set of axioms, which are more measurement-theoretical in nature. They also parallel the informal conditions underlying the kind of complexity that the mhc entails.
We trace the first four years of the new theoretical discourse on the definition order 16 of hierarchical complexity. Tasks performed at this order are similarly classified as stage 16 performances. Until this current discourse began, the highest order identified using the mhc was order 15, named cross-paradigmatic. In different groupings, several mhc theorists have discussed the properties and definition of this new order. To this point, an explicitly collaborative effort has yet to be undertaken. To reach agreement on definition and properties of order 16 and task performances at that order will likely require us to agree on more complex than usual hierarchical complexity-based scoring criteria and inter-rater standards. To meet these new challenges, these criteria and standards must be precise enough, complex enough, and general enough to apply across the uncommonly disparate and high-level examples proposed thus far as performances at stage 16. Since these methodological foundations have not yet been developed, to date our discourse is comprised of some who consider the process of defining the new order and empirically demonstrating it further along than others do. This theoretical development terrain promise intense and promising work ahead on this breakthrough in applying the mhc, its contributions to behavioral development theory, and the measurement of the most complex human accomplishments recognized thus far.
The latest research on developmental stage, according to the Model of Hierarchical Complexity (MHC), shows that there is only 1 domain, that stage develops as log 2 (age) and that the number of neurons of a species can predict the mean stage attained by that species. This can be interpreted as saying that biology controls stage. However, humans attain different stages and the biological mechanism that limits stage is still unknown. Based on these findings, we argue that cognitive neuroscience studies of human intelligence should shift from the general laws that govern development and brain maturation to focusing on interindividual differences across development, so as to complete the picture of human cognition beyond statistical norms. We here propose a study that looks for differences in patterns of the brain activation between subjects performing below and above formal stages. What differentiates this study from others that have been conducted in the field of developmental psychology and cognitive neuroscience is that this will explain for the first time not how, but why, some individuals are hardwired to perform at higher stages than others. We intend to analyze the data across different hierarchical complexity tasks and extract a saturation index (SI) that informs about the processing load of problem solving. Second, we compare the SI across subjects who attained different stages. This knowledge will provide for understanding the biological basis of cognition, for improving the behavioral predictive MHC, and for developing a connectionist model of cognition that emulates development throughout life.
The rate of international migration has reached unprecedented levels over the past few decades (Schwartz, Unger, Zamboanga, & Szapocznik, 2010). According to the International Organization for Migration, the number of migrants worldwide was 244 million in 2015. Current literature in cross-cultural adaptability describes acculturation as a 4-phase process, namely honeymoon, crisis, adjustment, and biculturalism (Sawyer, 2012). This approach characterizes all migrants equally, regardless of the type of migrant. This approach however is missing other factors such as an individual's developmental stage, needs, interests, and exposed/reinforcing memes. Orientations of these characteristics can vary drastically from one immigrant to another, and play an important role in acculturation outcomes. For a successful transition, immigrants and natives alike need to form successful relationships, while immigrants discover how systems work in the new country, and learn how to pursue their goals using the new resources in their new environment. These adaptive behaviors displayed by immigrants and natives are analyzed using stage theory, Maslow's hierarchy of needs, interests, and memes. In this article, we explore why some individuals and groups adapt better than others. A low-acculturated immigrant operates at a lower stage, may have been and continue to be exposed to memes that are less diverse, have lower needs, and choose more traditional careers. A highly acculturated immigrant operates at a higher stage, may be exposed to more diverse memes, have higher needs and choose more nontraditional careers. Through this analysis, we hope to contribute to a better understanding of immigration processes of different immigrants.
Simplified generation of all mechanical assembly sequences," IEEE J.Abstract-This paper analyzes adaptive algorithm 3 (AA3) of adaptive self-organizing concurrent systems (ASOCS) and proves that AA3 correctly fulfills the rules presented. Several different models for ASOCS have been developed. AA3 uses a distributed mechanism for implementing rules so correctness is not obvious. An ASOCS is an adaptive network composed of many simple computing elements operating in parallel. An ASOCS operates in one of two modes: learning and processing. In learning mode, rules are presented to the ASOCS and incorporated in a self-organizing fashion. In processing mode, the ASOCS acts as a parallel hardware circuit that performs the function defined by the learned rules.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.