Group Decision-Making from an Evolutionary/Adaptationist Perspective

Over the 20 years that Group Processes & Intergroup Relations has been in existence, evolutionary theory has begun to play a larger role in our understanding of human social behavior. Theory and research on group decision-making is no exception and the present paper attempts to briefly highlight how an evolutionary/adaptationist perspective has informed our understanding of how groups reach consensus and make collective choices. In addition, we attempt to show that humans are not the only species that use group processes to make important choices. Looking for similarities and continuities among research domains with different species should lead to a more unified and informed understanding of group decision-making processes and outcomes

Learning While Deciding in Groups

Groups are used to make many important societal decisions. Similar to individuals, by paying attention to the information available during the decision processes and the consequences of the decisions, groups can learn from their decisions as well. In addition, group members can learn from each other by exchanging information and being exposed to different perspectives. However, groups make decisions in many different ways and the potential and actual learning that takes place will vary as a function of the manner in which groups reach consensus. This chapter reviews the literature on group decision making with a special emphasis on how and when group decision making leads to learning. We argue that learning is possible in virtually any group decision-making environment, but freely interacting groups create the greatest potential for learning. We also discuss when and why groups may not always take advantage of the learning potential

Decisional and Behavioral Procrastination: How They Relate to Self-Discrepancies

A self-discrepancy is a gap between the perceived real self and other standards like the ideal self. One hundred and eighty-one college students completed a self-report measure of self-discrepancies and decisional and behavioral procrastination. Regression analysis showed that overall dysfunctional procrastination (the composite measure of both kinds of procrastination) significantly varied as a function of self-discrepancies. The amount of variance explained was small. Those scoring high in self-discrepancies were more likely to be dysfunctional procrastinators than those scoring low. The discrepancy between the actual-self and the ought-to self was the strongest predictor of dysfunctional procrastination. When decisional and behavioral procrastination were analyzed separately, only decisional procrastination significantly varied as a function of self-discrepancies

Conversion of sub-tropical native vegetation to introduced conifer forest: Impacts on below-ground and above-ground carbon pools

Land-use change can have a major influence on soil organic carbon (SOC) and above-ground C pools. We assessed a change from native vegetation to introduced Pinus species plantations on C pools using eight paired sites. At each site we determined the impacts on 0–50 cm below-ground (SOC, charcoal C, organic matter C, particulate organic C, humic organic C, resistant organic C) and above-ground (litter, coarse woody debris, standing trees and woody understorey plants) C pools. In an analysis across the different study sites there was no significant difference (P > 0.05) in SOC or above-ground tree C stocks between paired native vegetation and pine plantations, although significant differences did exist at specific sites. SOC (calculated based on an equivalent soil mass basis) was higher in the pine plantations at two sites, higher in the native vegetation at two sites and did not differ for the other four sites. The site to site variation in SOC across the landscape was far greater than the variation observed with a change from native vegetation to introduced Pinus plantation. Differences between sites were not explained by soil type, although tree basal area was positively correlated with 0–50 cm SOC. In fact, in the native vegetation there was a significant linear relationship between above-ground biomass and SOC that explained 88.8% of the variation in the data. Fine litter C (0–25 mm diameter) tended to be higher in the pine forest than in the adjacent native vegetation and was significantly higher in the pine forest at five of the eight paired sites. Total litter C (0–100 mm diameter) increased significantly with plantation age (R2 = 0.64). Carbon stored in understorey woody plants (2.5–10 cm DBH) was higher in the native vegetation than in the adjacent pine forest. Total site C varied greatly across the study area from 58.8 Mg ha−1 at a native heathland site to 497.8 Mg ha−1 at a native eucalypt forest site. Our findings suggest that the effects of change from native vegetation to introduced Pinus sp. forest are highly site-specific and may be positive, negative, or have no influence on various C pools, depending on local site characteristics (e.g. plantation age and type of native vegetation)

The development of a space climatology: 3. Models of the evolution of distributions of space weather variables with timescale

We study how the probability distribution functions of power input to the magnetosphere Pα and of the geomagnetic ap and Dst indices vary with averaging timescale, , between 3 hours and 1 year. From this we develop and present algorithms to empirically model the distributions for a given and a given annual mean value. We show that lognormal distributions work well for ap, but because of the spread of Dst for low activity conditions, the optimum formulation for Dst leads to distributions better described by something like the Weibull formulation. Annual means can be estimated using telescope observations of sunspots and modelling, and so this allows the distributions to be estimated at any given between 3 hour and 1 year for any of the past 400 years, which is another important step towards a useful space weather climatology. The algorithms apply to the core of the distributions and can be used to predict the occurrence rate of “large” events (in the top 5% of activity levels): they may contain some, albeit limited, information relevant to characterizing the much rarer “superstorm” events with extreme value statistics. The algorithm for the Dst index is the more complex one because, unlike ap, Dst can take on either sign and future improvements to it are suggested