The modifier effect and property mutability

The modifier effect is the reduction in perceived likelihood of a generic property sentence, when the head noun is modified. We investigated the prediction that the modifier effect would be stronger for mutable than for central properties, without finding evidence for this predicted interaction over the course of five experiments. However Experiment 6, which provided a brief context for the modified concepts to lend them greater credibility, did reveal the predicted interaction. It is argued that the modifier effect arises primarily from a general lack of confidence in generic statements about the typical properties of unfamiliar concepts. Neither prototype nor classical models of concept combination receive support from the phenomenon

Smarter choices ?changing the way we travel. Case study reports

This report accompanies the following volume:Cairns S, Sloman L, Newson C, Anable J, Kirkbride A and Goodwin P (2004)Smarter Choices ? Changing the Way We Travel. Report published by theDepartment for Transport, London, available via the ?Sustainable Travel? section ofwww.dft.gov.uk, and from http://eprints.ucl.ac.uk/archive/00001224/

Smarter choices - changing the way we travel

Summary: In recent years, there has been growing interest in a range of initiatives, which are now widelydescribed as 'soft' transport policy measures. These seek to give better information and opportunities,aimed at helping people to choose to reduce their car use while enhancing the attractiveness ofalternatives. They are fairly new as part of mainstream transport policy, mostly relativelyuncontroversial, and often popular. They include:. Workplace and school travel plans;. Personalised travel planning, travel awareness campaigns, and public transport information andmarketing;. Car clubs and car sharing schemes;. Teleworking, teleconferencing and home shopping.This report draws on earlier studies of the impact of soft measures, new evidence from the UK andabroad, case study interviews relating to 24 specific initiatives, and the experience of commercial,public and voluntary stakeholders involved in organising such schemes. Each of the soft factors isanalysed separately, followed by an assessment of their combined potential impact.The assessment focuses on two different policy scenarios for the next ten years. The 'high intensity'scenario identifies the potential provided by a significant expansion of activity to a much morewidespread implementation of present good practice, albeit to a realistic level which still recognisesthe constraints of money and other resources, and variation in the suitability and effectiveness of softfactors according to local circumstances. The 'low intensity' scenario is broadly defined as aprojection of the present (2003-4) levels of local and national activity on soft measures.The main features of the high intensity scenario would be. A reduction in peak period urban traffic of about 21% (off-peak 13%);. A reduction of peak period non-urban traffic of about 14% (off-peak 7%);. A nationwide reduction in all traffic of about 11%.These projected changes in traffic levels are quite large (though consistent with other evidence onbehavioural change at the individual level), and would produce substantial reductions in congestion.However, this would tend to attract more car use, by other people, which could offset the impact ofthose who reduce their car use unless there are measures in place to prevent this. Therefore, thoseexperienced in the implementation of soft factors locally usually emphasise that success depends onsome or all of such supportive policies as re-allocation of road capacity and other measures toimprove public transport service levels, parking control, traffic calming, pedestrianisation, cyclenetworks, congestion charging or other traffic restraint, other use of transport prices and fares, speedregulation, or stronger legal enforcement levels. The report also records a number of suggestionsabout local and national policy measures that could facilitate the expansion of soft measures.The effects of the low intensity scenario, in which soft factors are not given increased policy prioritycompared with present practice, are estimated to be considerably less than those of the high intensityscenario, including a reduction in peak period urban traffic of about 5%, and a nationwide reductionin all traffic of 2%-3%. These smaller figures also assume that sufficient other supporting policies areused to prevent induced traffic from eroding the effects, notably at peak periods and in congestedconditions. Without these supportive measures, the effects could be lower, temporary, and perhapsinvisible.Previous advice given by the Department for Transport in relation to multi-modal studies was that softfactors might achieve a nationwide traffic reduction of about 5%. The policy assumptionsunderpinning this advice were similar to those used in our low intensity scenario: our estimate isslightly less, but the difference is probably within the range of error of such projections.The public expenditure cost of achieving reduced car use by soft measures, on average, is estimated atabout 1.5 pence per car kilometre, i.e. £15 for removing each 1000 vehicle kilometres of traffic.Current official practice calculates the benefit of reduced traffic congestion, on average, to be about15p per car kilometre removed, and more than three times this level in congested urban conditions.Thus every £1 spent on well-designed soft measures could bring about £10 of benefit in reducedcongestion alone, more in the most congested conditions, and with further potential gains fromenvironmental improvements and other effects, provided that the tendency of induced traffic to erodesuch benefits is controlled. There are also opportunities for private business expenditure on some softmeasures, which can result in offsetting cost savings.Much of the experience of implementing soft factors is recent, and the evidence is of variable quality.Therefore, there are inevitably uncertainties in the results. With this caveat, the main conclusion isthat, provided they are implemented within a supportive policy context, soft measures can besufficiently effective in facilitating choices to reduce car use, and offer sufficiently good value formoney, that they merit serious consideration for an expanded role in local and national transportstrategy.AcknowledgementsWe gratefully acknowledge the many contributions made by organisations and individuals consultedas part of the research, and by the authors of previous studies and literature reviews which we havecited. Specific acknowledgements are given at the end of each chapter.We have made extensive use of our own previous work including research by Lynn Sloman funded bythe Royal Commission for the Exhibition of 1851 on the traffic impact of soft factors and localtransport schemes (in part previously published as 'Less Traffic Where People Live'); and by SallyCairns and Phil Goodwin as part of the research programme of TSU supported by the Economic andSocial Research Council, and particularly research on school and workplace travel plans funded bythe DfT (and managed by Transport 2000 Trust), on car dependence funded by the RAC Foundation,on travel demand analysis funded by DfT and its predecessors, and on home shopping funded byEUCAR. Case studies to accompany this report are available at: http://eprints.ucl.ac.uk/archive/00001233

An adaptive policy-based framework for network services management

This paper presents a framework for specifying policies for the management of network services. Although policy-based management has been the subject of considerable research, proposed solutions are often restricted to condition-action rules, where conditions are matched against incoming traffic flows. This results in static policy configurations where manual intervention is required to cater for configuration changes and to enable policy deployment. The framework presented in this paper supports automated policy deployment and flexible event triggers to permit dynamic policy configuration. While current research focuses mostly on rules for low-level device configuration, significant challenges remain to be addressed in order to:a) provide policy specification and adaptation across different abstraction layers; and, b) provide tools and services for the engineering of policy-driven systems. In particular, this paper focuses on solutions for dynamic adaptation of policies in response to changes within the managed environment. Policy adaptation includes both dynamically changing policy parameters and reconfiguring the policy objects. Access control for network services is also discussed.Accepted versio

Smarter Choices : Assessing the Potential to Achieve Traffic Reduction Using 'Soft Measures'

Peer reviewedPostprin

Feature integration in natural language concepts

Two experiments measured the joint influence of three key sets of semantic features on the frequency with which artifacts (Experiment 1) or plants and creatures (Experiment 2) were categorized in familiar categories. For artifacts, current function outweighed both originally intended function and current appearance. For biological kinds, appearance and behavior, an inner biological function, and appearance and behavior of offspring all had similarly strong effects on categorization. The data were analyzed to determine whether an independent cue model or an interactive model best accounted for how the effects of the three feature sets combined. Feature integration was found to be additive for artifacts but interactive for biological kinds. In keeping with this, membership in contrasting artifact categories tended to be superadditive, indicating overlapping categories, whereas for biological kinds, it was subadditive, indicating conceptual gaps between categories. It is argued that the results underline a key domain difference between artifact and biological concepts

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

The Pudding of Trust

Trust - "reliance on the integrity, ability, or character of a person or thing" - is pervasive in social systems. We constantly apply it in interactions between people, organizations, animals, and even artifacts. We use it instinctively and implicitly in closed and static systems, or consciously and explicitly in open or dynamic systems. An epitome for the former case is a small village, where everybody knows everybody, and the villagers instinctively use their knowledge or stereotypes to trust or distrust their neighbors. A big city exemplifies the latter case, where people use explicit rules of behavior in diverse trust relationships. We already use trust in computing systems extensively, although usually subconsciously. The challenge for exploiting trust in computing lies in extending the use of trust-based solutions, first to artificial entities such as software agents or subsystems, then to human users' subconscious choices