Micro-meso-macro

Building on the ontology of evolutionary realism recently proposed by Dopfer and Potts (forthcoming), we develop an analytical framework for evolutionary economics with a micro-meso-macro architecture. The motive for reconception is to make clear the highly complex and emergent nature of existence and change in economic evolution. For us, the central insight is that an economic system is a population of rules, a structure of rules, and a process of rules. The economic system is a rule-system contained in what we call the meso. From the evolutionary perspective, one cannot directly sum micro into macro. Instead, we conceive of an economic system as a set of meso units, where each meso consists of a rule and its population of actualizations. The proper analytical structure of evolutionary economics is in terms of micro-meso-macro. Micro refers to the individual carriers of rules and the systems they organize, and macro consists of the population structure of systems of meso. Micro structure is between the elements of the meso, and macro structure is between meso elements. The upshot is an ontologically coherent framework for analysis of economic evolution as change in the meso domain - in the form of what we call a meso trajectory - and a way of understanding the micro-processes and macro-consequences involved. We believe that the micro-meso-macro analytical framework can greatly enhance the focus, clarity, and, ultimately, power, of evolutionary economic theory.

Mud, algae or seagrass: An assessment of remote sensing techniques for mapping inter-tidal seagrass cover and composition in turbid tropical environments

Large areas of tropical sub- and inter-tidal seagrass beds occur in highly turbid environments and cannot be mapped through the water column. The purpose of this project was to determine if and how airborne and satellite imaging systems could be used to map inter-tidal seagrass properties along the wet-tropics coast in north Queensland, Australia. The work aimed to: (1) identify the minimum level of seagrass foliage cover that could be detected from airborne and satellite imagery; and (2) define the minimum detectable differences in seagrass foliage cover in exposed intertidal seagrass beds. High resolution spectral-reflectance data (2040 bands, 350 – 2500nm) were collected over 40cm diameter plots from 240 sites on Magnetic Island, Pallarenda Beach and Green Island in North Queensland at spring low tides in April 2006. The seagrass species sampled were: Thalassia hemprechii, Halophila ovalis, Halodule uninerivs; Syringodium isoetifolium, Cymodocea serrulata, and Cymodoea rotundata. Digital photos were captured for each plot and used to derive estimates of seagrass species cover, epiphytic growth, micro- and macro-algal cover, and substrate colour. Sediment samples were also collected and analysed to measure the concentration of Chlorophyll-a associated with benthic micro-algae. The field reflectance spectra were analysed in combination with their corresponding seagrass species foliage cover levels to establish the minimum foliage projective cover required for each seagrass to be significantly different from bare substrate and substrate with algal cover. This analysis was repeated with reflectance spectra resampled to the bandpass functions of Quickbird, Ikonos, SPOT 5 and Landsat 7 ETM. Preliminary results indicate that conservative minimum detectable seagrass cover levels across most the species sampled were between 30%- 35% on dark substrates. Further analysis of these results will be conducted to determine their separability and satellite images and to assess the effects epiphytes and algal cover.

Gray's model of personality and aggregate level factor analysis

Previous research shows that correlations tend to increase in magnitude when individuals are aggregated across groups. This suggests that uncorrelated constellations of personality variables (such as the primary scales of Extraversion and Neuroticism) may display much higher correlations in aggregate factor analysis. We hypothesize and report that individual level factor analysis can be explained in terms of Giant Three (or Big Five) descriptions of personality, whereas aggregate level factor analysis can be explained in terms of Gray's physiological based model. Although alternative interpretations exist, aggregate level factor analysis may correctly identify the basis of an individual's personality as a result of better reliability of measures due to aggregation. We discuss the implications of this form of analysis in terms of construct validity, personality theory, and its applicability in general. Copyright (C) 2003 John Wiley Sons, Ltd.

Molecular motors-based micro- and nano-biocomputation devices

Protein molecular motors, which are natural nano-machines that convert the chemical energy into mechanical work for cellular motion, muscle contraction and cell division, have been integrated in the last decade in primitive nanodevices based on the motility of nano-biological objects in micro- and nano-fabricated structures. However, the motility of microorganisms powered by molecular motors has not been similarly exploited. Moreover, among the proposed devices based on molecular motors, i.e., nanosensors, nano-mechanical devices and nano-imaging devices, biocomputation devices are conspicuously missing. The present contribution discusses, based on the present state of the art nano- and micro-fabrication, the comparative advantages and disadvantages of using nano- and micro-biological objects in future computation devices. (c) 2006 Elsevier B.V. All rights reserved.

Reflexive OKN with partial and full field stimulation: Effects of age and light level

We recorded reflexive OKN in ten younger (32.3±5.98 years) and older (65.6±6.53) visually normal subjects under viewing conditions designed to differentiate M-pathway functioning from other pathways. Subjects were required to gaze straight ahead while viewing vertical gratings of either 0.43 or 1.08 cpd, drifting at either 5 or 20°/sec and presented at either 8 or 80% contrast. Gratings were presented as full field stimulation, central stimulation or peripheral (>15°) stimulation. The order of presentation of conditions was pseudo-randomised at two blocked light levels: ‘mesopic’ or twilight conditions (1.8 cdm-2) and ‘photopic’ or full light conditions (71.5 cdm-2). For the partial fields, central stimulation, mesopic light level, lower temporal frequencies (i.e. number of stripes passing per second) each contributed to greater OKN strength as measured by slow-phase velocity (SPV). For full field stimulation, and especially for higher temporal frequencies and low contrast, there was a significant interaction between age group × light level (p = 0.017): SPV diminished much more among the older than the younger group for the twilight condition compared to full light. Such a clear diminution in M-pathway sensitivity revealed by OKN response has important implications for everyday situations like crash avoidance under twilight driving conditions.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Workplace diversity: The moderating role of work context on the relationship between surface and deep level diversity and group functioning

This research examines the relationship between perceived group diversity and group conflict, and the moderating role of team context. Currentiy, diversity research predominantly focuses on surface and job-related dimensions, largely to the neglect of deep-level diversity (in terms of values, attitude and beliefs). First, this research hjfpothesised that all three dimensions of diversity would be positively related to group conflict, with deep-level diversity the strongest predictor of task. conflict. Second, it was hypothesised that team context would moderate the relationship between deep-level diversity and group conflict. Team context refers to the extent to which the work performed (1) has high consequences (in terms of health and well being for team members and others); (2) is relatively isolating, (3) requires a high reliance upon team members; (4) is volatile; and (5) interpersonal attraction and mutual helpfulness is essential. Two studies were conducted. The first study employed 44 part-time employees across a range of occupations, and the second study employed 66 full-time employees from a mining company in Australia. A series of hierarchical multiple regressions and moderated multiple regressions confirmed both hypotheses. Practical implications and future research directions are discussed.

Changes in social identity when joining a new group: The role of individual-level and group-level adaptation processes

While classic intergroup theories have specified the processes explaining situational shifts in social identification, the processes whereby social identities change more profoundly and become integrated within the self have to be proposed. To this aim, the present studies investigate the processes by which group members integrate a new social identity as they are joining a new group. Combining a social identity approach and stress and coping models, this research tests if social factors (i.e., needs satisfied by fellow group members, social support), have an impact on the adaptation strategies group members use to deal with the novelty of the situation and to fit into their new group (seeking information & adopting group norms vs. disengaging). These strategies, in turn, should predict changes in level of identification with the new social group over time, as well as enhanced psychological adjustment. These associations are tested among university students over the course of their first academic year (Study 1), and among online gamers joining a newly established online community (Study 2). Path analyses provide support for the hypothesised associations. The results are discussed in light of recent theoretical developments pertaining to intraindividual changes in social identities and their integration in the self.