Model Of Hierarchical Complexity Research Articles

Development of a Virtual System to Analyze the Learning of 3D Shapes

This study focuses on the development of a Virtual Environment application that interacts with a haptic device, to analyze the human process of learning 3 D shapes. The levels a person goes through in this learning process are established based on the Model of Hierarchical Complexity, translated into questions, and tested with a sample of users. The obtained results validate and demonstrate an increase in hierarchical complexity along the questions/stages and validate the Model, with most users following the concept that for a person to accomplish a higher hierarchical task, must be able to accomplish all the lower ones.

Comparisons of Animal “Smart” Using The First Four Stages of the Model of Hierarchical Complexity

The Model of Hierarchical Complexity is a behavioral model of development and evolution of the complexity of behavior. It is based on task analysis. Tasks are ordered in terms of their hierarchical complexity, which is an ordinal scale that measures difficulty. The hierarchical difficulty of tasks is categorized as the order of hierarchical complexity. Successful performance on a task is called the behavioral stage. This model can be applied to non-human animals, and humans. Using data from some of the simplest animals and also somewhat more complex ones, this analysis describes the four lowest behavioral stages and illustrate them using the behaviors of a range of simple organisms. For example, Stage 1 tasks, and performance on them, are addressed with automatic unconditioned responses. Behavior at this Stage includes sensing, tropisms, habituation and, other automatic behaviors. Single cell organisms operate at this Stage. Stage 2 tasks include these earlier behaviors, but also include respondent conditioning but not operant conditioning. Animals such as some simple invertebrates have shown respondent conditioning, but not operant conditioning. Stage 3 tasks coordinate three instances of these earlier tasks to make possible operant conditioning. These stage 3 performances are similar to those of some invertebrates and also insects. Stage 4 tasks organisms coordinate 2 or more circular sensory-motor task actions into a superordinate “concept”. This explanation of the early stages of the Model of Hierarchical Complexity may help future research in animal behavior, and comparative psychology.

A developmental and evolutionary theory of punishment

Summary Punishment and its avoidance are foundational to organisms’ behavior and evolutionary development. How do responses to unfavorable situations change across animal species? We address this question using a model of developmental and evolutionary complexity (Model of Hierarchical Complexity). Tasks are ordered in terms of an ordinal scale that measures difficulty (Order of Hierarchical Complexity). Successful completion of a task at a particular order is the behavioral stage of the organism on that task. This model applies to non-human and human-animals. Examples of organisms that perform tasks of punishment avoidance at each order are provided. The Model provides a useful way of explaining the differences between organisms that is consistent with evolutionary data. We argue that as animals become more complex, punishments become more harmful, but used at decreasing frequencies. However, punishment does not disappear at even the highest stages.

Using model-based scaffolds to support students solving context-based chemistry problems

ABSTRACTContext-based learning aims to make learning more meaningful by raising meaningful problems. However, these types of problems often require reflection and thinking processes that are more complex and thus more difficult for students, putting high demands on students’ problem-solving capabilities. In this paper, students’ approaches when solving context-based chemistry problems and effects of systematic scaffolds are analysed based on the Model of Hierarchical Complexity. Most answers were initially assigned to the lowest level of the model; higher levels were reached without scaffolds only by few students and by most students with scaffolds. The results are discussed with regard to practical implications in terms of how teachers could make use of context-based tasks and aligned scaffolds to help students in this activity.

An analysis of the Verbal Comprehension Index of the Wechsler Adult Intelligence Scale—Fourth Edition (WAIS–IV) using the Model of Hierarchical Complexity (MHC): Why might stage be a better measure of “smarts” than verbal IQ?

An analysis of the Verbal Comprehension Index of the Wechsler Adult Intelligence Scale—Fourth Edition (WAIS–IV) using the Model of Hierarchical Complexity (MHC): Why might stage be a better measure of “smarts” than verbal IQ?

Assessing complexity in learning outcomes – a comparison between the SOLO taxonomy and the model of hierarchical complexity

An important aspect of higher education is to educate students who can manage complex relationships and solve complex problems. Teachers need to be able to evaluate course content with regard to complexity, as well as evaluate students’ ability to assimilate complex content and express it in the form of a learning outcome. One model for evaluating complexity is the Structure of Observed Learning Outcomes (SOLO) taxonomy. The aim of this analysis is to address the limitations of the SOLO taxonomy in detecting the more subtle differences of the learning outcomes and to clarify the concept of learning modes. This is done by analysing the SOLO taxonomy by means of the model of hierarchical complexity (MHC). The two models are compared by examining their respective theoretical background, the definitions and descriptions of the stages of each model, as well as through evaluating examples illustrating the SOLO levels using MHC. The two models can be viewed as compatible, with MHC also being able to put the SOLO taxonomy in an adult development context, thereby emphasising the importance of developing the students’ access to complex thinking.

Cognitive complexity, religious cognition, cognitive development, and religious judgment: An empirical study of relationships amongst “normal” and “gifted” young people.

This study examined whether level of cognitive complexity in religious cognition, using measures rooted in the Model of Hierarchical Complexity, mediates the relationship between level of general cognitive development, and religious judgment, in children and adolescents. The study was conducted with 189 children and adolescents drawn from Catholic schools in France. General cognitive development level was measured using the WISC and WAIS. Level of cognitive complexity in religious cognition was measured using the Religious Cognition Questionnaire: PastorParishioner Scenario (RCQ). Religious judgment levels were measured using the Religious Reflection Questionnaire (RRQ). Results indicate that the relationship between IQ and religious judgment is mediated by level of complexity in religious cognition. The results provide further empirical evidence for the conceptual validity and research utility of the Model of Hierarchical Complexity in the domain of religious cognition, and the power of the concept of complexity in religious cognition for explaining relationships between general cognitive development using IQ measures and other domains where the judgment of social, moral, and philosophical issues are concerned. The results also provide further empirical evidence for the distinctive, and relatively advanced, capacity of gifted young people to think critically about religious and philosophical issues.

Stage of development and million dollar per year earning from sales.

This article presents cases showing that people with higher stages in the Model of Hierarchical Complexity can handle more complicated job responsibilities. Account Executives and Consultants from a training company participated in the study. According to the Hierarchical Complexity Scoring System (HCSS; Commons, Miller, Goodheart, & Danaher-Gilpin, 2005) participants’ person scores are based on the highest stage item the participant performed in the interview. Six of 9 Accountant Executives and 4 of 15 Consultants performed at the Metasystematic Stage 13. The study indicated that people who perform at the Metasystematic Stage 13 will have a better chance of achieving higher compensation than people performing at the Formal Stage 11 or Systematic Stage 12.

Stages of investing generated using the model of hierarchical complexity.

Stage 10 As developmental stage increases, individuals possess the skills, knowledge, and understanding of the lower stages, as well as a new set of skills, knowledge, and understanding that allow the jump to the higher stage. At the Abstract Stage 10, the decision-making process on investment strategy is heavily influenced by outside sources. A person at the Abstract Stage follows the crowd as they buy low and sell high because that is what others are doing. They listen to others to find a “highly rated” investment advisor and then do what their advisor says no matter what the performance. This directly leads to large fees, low returns, and a high likelihood of losing money. They do not yet understand ratios and percentages in the context of interest rates and percent fees on mutual funds, nor do they understand percent inflation and why the return on bonds may not keep up with inflation. The “Wealth Effect” (Darby, 1987; Jelveh, 2008; Zubin, 2008) is an economic term where an increase in perceived or actual wealth leads to an increase in spending, or vice versa with a perceived or actual decrease in wealth. Temporary wealth changes have a smaller effect on consumption changes than permanent wealth changes. With the wealth effect people psychologically associate higher net worth with having more disposable income. This is evident at Abstract Stage 9 where big gains in portfolio values attributable to bull markets make people feel secure about their wealth, so they spend more of it. At this stage, people tend to invest more at the height of markets and overconfidence, caused by the wealth effect, leads to bigger losses when the market crashes. People also pull money out at the bottom of the market. They make overcorrections that are similar to those made by novice sailors. If one tries to turn too quickly, one will find that most times one ends up turning too far across the wind. One’s instinctive reaction is to turn back the other direction, most times ending up head to the wind instead. The process of overcorrecting, both in investing and on the water, is the result of riskaversion. This leads to smaller overall gains when the markets go backup. If the wealth effect is too strong, meaning people overcorrect for changes in the markets by selling too much when the prices are low and buying too much when the prices are high.

Analysing task design and students’ responses to context-based problems through different analytical frameworks

Background:Context-based learning approaches are used to enhance students’ interest in, and knowledge about, science. According to different empirical studies, students’ interest is improved by applying these more non-conventional approaches, while effects on learning outcomes are less coherent. Hence, further insights are needed into the structure of context-based problems in comparison to traditional problems, and into students’ problem-solving strategies. Therefore, a suitable framework is necessary, both for the analysis of tasks and strategies.Purpose:The aim of this paper is to explore traditional and context-based tasks as well as students’ responses to exemplary tasks to identify a suitable framework for future design and analyses of context-based problems. The paper discusses different established frameworks and applies the Higher-Order Cognitive Skills/Lower-Order Cognitive Skills (HOCS/LOCS) taxonomy and the Model of Hierarchical Complexity in Chemistry (MHC-C) to analyse traditional tasks and students’ responses.Sample:Upper secondary students (n=236) at the Natural Science Programme, i.e. possible future scientists, are investigated to explore learning outcomes when they solve chemistry tasks, both more conventional as well as context-based chemistry problems.Design and methods:A typical chemistry examination test has been analysed, first the test items in themselves (n=36), and thereafter 236 students’ responses to one representative context-based problem. Content analysis using HOCS/LOCS and MHC-C frameworks has been applied to analyse both quantitative and qualitative data, allowing us to describe different problem-solving strategies.Results:The empirical results show that both frameworks are suitable to identify students’ strategies, mainly focusing on recall of memorized facts when solving chemistry test items. Almost all test items were also assessing lower order thinking. The combination of frameworks with the chemistry syllabus has been found successful to analyse both the test items as well as students’ responses in a systematic way. The framework can therefore be applied in the design of new tasks, the analysis and assessment of students’ responses, and as a tool for teachers to scaffold students in their problem-solving process.Conclusions:This paper gives implications for practice and for future research to both develop new context-based problems in a structured way, as well as providing analytical tools for investigating students’ higher order thinking in their responses to these tasks.

There is only one stage domain.

The study used the model of hierarchical complexity (MHC) to test the theory that different domains in development would develop in synchrony, allowing an individual to solve tasks from various domains using the same mental structure for each task. The MHC instruments used were the empathy, helper person, counselor patient, breakup, caregiver, algebra, balance beam, infinity and laundry instruments. The instruments can be categorized as belonging to two different subdomains, the social subdomain, and the logic/mathematics/physical sciences subdomain. Instruments in this social subdomain measure developmental stage in a variety of social contexts. These social contexts included empathy for person after an accident, guidance and assistance by a helper, counseling patients, understanding romantic breakups, and caring for children and infants. The other subdomain is composed of mathematical (algebra & infinity), logical (laundry), and physical science (balance beam). In order to conclude how related the performances were, three analyses were carried out. First, Rasch analysis yielded person scores akin to person stage scores. Second, regression analysis was conducted to assess how well the order of hierarchical complexity (OHC) of the items predicted the Rasch difficulty of the items. Third a principal axis factoring was carried out with the person Rasch scores for every instrument. Irrespective of domains, if each instrument loaded on the first factor with all the factor scores over .7 and if the first factor accounted for more than 70% of the variance, then that would show that all instruments were part of a single domain. In each case the MHC accounted for a large amount of variance with r values over .7. The principal axis factoring showed that person scores on each instrument loaded on the first factor (90.51% of the variance). All the factor scores on the first factor were over .85. There were very low loadings only on the second factor (4.947% of the variance). This implies that the instruments from the social subdomain and instruments from the logic/mathematics/physical sciences belong to a single domain.

Advances in the model of hierarchical complexity (MHC).

The model of hierarchical complexity (MHC) has a long history that only in very recent years resulted in its formal specification as a general model. A brief history of the origins of the notion of hierarchical complexity follows. This is done in order to identify what the steps were and also those who played key roles in the model’s development over these many years. In tracing the evolution of the MHC, there are four periods, each involving different people. The two earliest periods were Commons’ pre-college years, college and graduate school years, followed by a period of more active and direct development from 1973 to 1982. After the model was developed, there were advances in the period from 1982 to present.

The small effects of non-hierarchical complexity variables on performance.

Even when the results show that most of the difficulty in solving problems is explained by the hierarchical complexity of the item, there are still variables that help in small ways in predicting how well items measure difficulty. One must understand what these variables are and take them into consideration when analyzing data from instruments designed to measure the impact of the order of hierarchical complexity of items. This study was designed to test the effect of small variables on task performance. The variables tested were hierarchical complexity, place in order, the number of calculations needs, the size of the numbers, and the causal variable position. Participants were asked to solve problems from task sequences from the logic/mathematics/physical science subdomains. The four instruments used were the algebra, balance beam, infinity and laundry instruments. These instruments were based on the model of hierarchical complexity (MHC). Participants were asked to first complete the laundry task sequence and move to the next task sequence. Items from each instrument were analyzed individually and as a group. A Rasch analysis was performed on all the items from all the instruments. The variables thought to have an effect were coded. The coded variables were then analyzed using stepwise regression. A stepwise regression was used and the small variables were tested with and without hierarchical complexity as a factor. The variables were regressed against the stage score of the items. For all four instruments stepwise regression with hierarchical complexity as one of the variable accounted for about 95% of the variance and the β was greater than 0.9. Stepwise regression with all the other variables except hierarchical complexity accounted for relatively lower variance and β. The results showed that order of hierarchical complexity has a very strong predictive role and accounts for most of the variance. The other variables only made very small contributions.

A model of stage change explains the average rate of stage of development and its relationship to the predicted average stage (“smarts”).

A number of different previous methods for measuring “smarts” have led to the model of hierarchical complexity (MHC), a context free neo-Piagetian mathematical model of behavioral complexity. It provides a way to classify tasks as to their hierarchical complexity. Using the model of hierarchical complexity, this study examines how differences in rate of stage change results in a difference in the highest average stage (smarts”) attained by 70 year old adults. The average stage of development (“smarts”) was shown to be predicted by the log of age with an r = .79. It uses data from Colby, Kohlberg, Gibbs, Lieberman (1983) to test the model. It also predicts that on the average there is one stage of development during adulthood.

The stage of development of a species predicts the number of neurons.

Does the complexity of an organism’s behavior predict the number of neurons in an organism’s brain? In the model of hierarchical complexity, the behavioral stage of any organism can be assessed. These behaviors fall into discrete stages. The behavioral stage of development of an organism is defined by the highest order task that an organism has been observed performing. In this study, literature was reviewed to find animals where a neuron count had been taken, and to find behavioral studies to score for stage of development. Once those determinations were made, a power regression analysis addressed the question of whether the behavioral stage of development at which a species operating at predicts the number of neurons an organism has. The relationship between these two variables was r (17) = 0.874. These findings imply developing to the next higher stage requires an increase in the number of neurons a species has. The evolutionary benefit from a species evolving to have more neurons may be driven by reinforcement contingencies in the environmental niche that species occupies. If these reinforcement contingencies are one order of hierarchical complexity higher than the stage the species operates at, then the species must increase the number of neuronal connections; this increase reaches a maximum dictated by the number of neurons, so there is a time when the species must evolve more neurons. to perform the comparatively more hierarchically complex tasks required to attain new reinforcement. Therefore it is the attraction of higher stage reinforcers that drives neural development. This neurological correlation for behavioral complexity shows that there is a countable amount of processing power that limits the rate of stage change in a lifetime. The accuracy with which stage of development predicts the number of neurons cast behavioral development as a driving force in neuronal evolution.

Fractal model of nonlinear hierarchical complexity: Measuring transition dynamics as fractals of themselves.

Fractal transition theory and measurement enable fine-grained analysis of the most seemingly-chaotic of the developmental transition phases. The explication of the fractal nature of those transition dynamics informs study of learning, decision making, and complex systems in general. A hallmark of the fractal measure is the use of thesis-organized transition measures that are orthogonal to time. Using this method, unpredictable behaviors become “rational” when understood in terms of attractors within developmental processes. An implication for nonlinear science is to transform data otherwise interpreted as incoherent “white noise” into the coherent fractals of the “pink noise” dimension. By integrating Commons et al’s Model of Hierarchical Complexity ( mhc) and this nonlinear model of the fractal transitional orders of hierarchical complexity, a unified mathematical theory of behavioral development will be possible. Such a new formal theory would account for the entire span of behavioral development’s equilibrium states and phase transitions, from lowest to highest orders of complexity. The mathematical expressions for the transitional orders of hierarchical complexity must be developed and integrated with the existing mhc.

Relationship among measures within the social and moral development domain.

This paper investigates using the Model of Hierarchical Complexity (MHC) as a framework to study individual’s stages of moral understanding. As an improvement from traditional stages of moral development, 15 stages of moral understanding were generated using the Model of Hierarchical Complexity. Data were collected in four separate studies on how participants make choices in specific moral dilemmas. Each study presented five or six vignettes of arguments, each constructed to have different Orders of Hierarchical Complexity. Participants rated the quality of arguments on a 1 to 6 scale. A Rasch analysis produced stage scores for each of the stories. The Rasch scores were regressed against the Order of Hierarchical Complexity of each vignette. These were Counselor-Patient: r (3) = .992; Anti-Death-Penalty: r(3) = .919; Incest –No Report: r(3) = .916; Incest – Report: r(3) = .624. The result showed that Rasch scores of vignettes were predicted by their Orders of Hierarchical Complexity, suggesting that the Model of Hierarchical Complexity was a good framework to study stage of moral understanding.

The model of hierarchical complexity as a measurement system.

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 operations, and the axioms, followed by an articulation of emerging properties from axioms, and then a description 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.

Cognitive basis for corruption and attitudes towards corruption in organizations viewed from a structuralist adult developmental meta-perspective.

The paper focuses on corruption and attitudes towards corruption in organizations. It proposes an interdisciplinary framework for reassessing them. It is argued that an integrative theoretical and analytical framework based on the Model of Hierarchical Complexity (mhc) can provide new insights on corruption. Furthermore the proposed framework offers new theoretical horizons for understanding and evaluating public and scientific discourses on corruption. This approach compensates for frequent shortcomings and disciplinary reductionisms in large parts of the social science literature on corruption. It can thus offer a substantially new outlook on the field of behavioral ethics in organizations based on a meta-systematic theory integration.

The construction and validation of a developmental test for stage identification: Two exploratory studies.

The present work presents two exploratory studies about the construction and validation of the Inductive Reasoning Developmental Test (irdt), a forty-eight items test based on the Model of Hierarchical Complexity. The first version of the test was administered to a convenience sample composed by 167 Brazilian people (50.3% men) aged between 6 to 58 years (m = 18.90, sd = 9.70). The Rasch Model was applied, and the result shows reliability of .97 for the full scale. The Infit mean was .87 (sd = .28; Max = 1.69; Min = .39), and the person reliability was .95. The one sample t-tests showed significant spacing of Rasch scores between items of adjacent orders of hierarchical complexity, with large effect size. The second study was conducted in order to overcome some of the test’s limitations found in the first study. The revised irdt were administered to a convenience sample composed of 188 Brazilian people (57.7% women) aged between 6 and 65 years (m = 21.45, sd = 14.31). The reliability for the full scale was .99, and its Infit mean was .94 (sd = .22; Max = 1.46; Min = .56). The person reliability was .95. The one sample t-tests showed significant spacing of Rasch scores between items of adjacent orders of hierarchical complexity, with large effect size. The paper finishes with a discussion about the necessity and importance to focus on the vertical complexity of the items in any test designed to identify developmental stages.