The Cooperative Work of Gaming: Orchestrating a Mobile SMS Game

This paper focuses on orchestration work in the first iteration of a mobile game called Day Of The Figurines, which explores the potential to exploit text messaging as a means of creating an engaging gaming experience. By focusing on orchestration we are especially concerned with the ‘cooperative work that makes the game work’. While the assemblage or family of orchestration practices uncovered by our ethnographic study are specific to the game – including the ways in which behind the scenes staff make sense of messages, craft appropriate responses, and manage and track the production of gameplay narratives as the game unfolds – orchestration work is of general significance to our understanding of new gaming experiences. The focus on orchestration work reveals that behind the scenes staff are co-producers of the game and that the playing of games is, therefore, inseparably intertwined with their orchestration. Furthermore, orchestration work is ‘ordinary’ work that relies upon the taken for granted skills and competences of behind the scenes staff; ‘operators’ and ‘authors’ in this case. While we remain focused on the specifics of this game, explication of the ordinary work of orchestration highlights challenges and opportunities for the continued development of gaming experiences more generally. Indeed, understanding the specificities of orchestration work might be said to be a key ingredient of future development.     

‘Working Well, Together’: Arts-Based Research and the Cultural Future of Small Cities

This article offers a research update on a 3-year programme initiated by the Kamloops Art Gallery and the University College of the Cariboo in Kamloops, British Columbia. The programme is supported by a ‘Community–University Research Alliance’ grant from the Social Sciences and Humanities Research Council of Canada, and the collaboration focuses on the cultural future of small cities – on how cultural and arts organisations work together (or fail to work together) in a small city setting. If not by definition, then certainly by default, ‘culture’ is associated with big city life: big cities are equated commonly with ‘big culture’; small cities with something less. The Cultural Future of Small Cities research group seeks to provide a more nuanced view of what constitutes culture in a small Canadian city. In particular, the researchers are exploring notions of social capital and community asset building: in this context, ‘visual and verbal representation’, ‘home’, ‘community’ and the need to define a local ‘sense of place’ have emerged as important themes. As the Small Cities programme begins its second year, a unique but key aspect has become the artist-as-researcher. Correspondence and offprint requests to: L. Dubinsky, Kamloops Art Gallery, 101–465 Victoria Street, Kamloops, BC V2C 2A9 Canada. Tel.: 250-828-3543; Email: ldubinsky@museums.ca     

Introducing contextual laddering to evaluate the likeability of games with children

It becomes more and more recognized that children should be involved in a product’s design and evaluation process. Many findings report on the methodology for usability research with children. However, there has been relatively little analysis of likeability research with children. In this paper, we propose the laddering method—traditionally a marketing method among adults—for likeability research in the domain of child–computer interaction. Three exploratory cases will be described. The cases report on the use of the laddering method with children aged between 7 and 16 to evaluate the likeability of two games. The lessons learnt about the use of the laddering method will be discussed in great detail. In order to adapt the laddering method to work with children, we recommend a variation of this method and call it the ‘contextual laddering method’.     

Computing LTS Regression for Large Data Sets

Data mining aims to extract previously unknown patterns or substructures from large databases. In statistics, this is what methods of robust estimation and outlier detection were constructed for, see e.g. Rousseeuw and Leroy (1987). Here we will focus on least trimmed squares (LTS) regression, which is based on the subset of h cases (out of n) whose least squares fit possesses the smallest sum of squared residuals. The coverage h may be set between n/2 and n. The computation time of existing LTS algorithms grows too much with the size of the data set, precluding their use for data mining. In this paper we develop a new algorithm called FAST-LTS. The basic ideas are an inequality involving order statistics and sums of squared residuals, and techniques which we call ‘selective iteration’ and ‘nested extensions’. We also use an intercept adjustment technique to improve the precision. For small data sets FAST-LTS typically finds the exact LTS, whereas for larger data sets it gives more accurate results than existing algorithms for LTS and is faster by orders of magnitude. This allows us to apply FAST-LTS to large databases.     

Cooperation, Coordination and Interpretation in Virtual Environments: Some Thoughts on Working-Together

The need for information technology-mediated cooperation seems obvious. However, what is not obvious is what this means and what social demands such cooperation may imply. To explore this is the intention of the paper. As a first step the paper performs an etymological analysis of the words telecooperation and telecoordination. Such an analysis indicates that cooperation happens when people engage in the production of a work as if ‘one mind or body’, where their activities fuse together in a way that makes the suggestion of separation seem incomprehensible. In the work they do not merely aim to achieve an outcome, they also ‘insert’ themselves ‘in’ the work in a way that makes it a human achievement rather than a mere product – this is cooperation as working-together. With this notion of cooperation in mind the paper then proceeds to analyse the social conditions for cooperation as working-together. It shows, using the work of Wittgenstein, that language is fundamental to cooperation and the sharing of knowledge – not language as a system for the exchange of information but language as a medium for the co-creation of a local way of doing, a local language, to capture the local distinctions that make a particular local activity significant and meaningful to the participants. The paper then proceeds to question this strong notion of cooperation. It argues that most cooperative activities tend not to conform with such stringent demands. The paper suggests that a cooperative problem is best viewed as a situation in which ambiguity is accepted as a structural element of the interaction. From this perspective the paper suggests that hermeneutics may be a productive way to understand the creation of shared interpretative spaces that makes mediated cooperation possible. The paper concludes with some implications for mediated cooperative work.     

MABA-MABA or Abracadabra? Progress on Human–Automation Co-ordination

In this paper we argue that substitution-based function allocation methods (such as MABA-MABA, or Men-Are-Better-At/Machines-Are-Better-At lists) cannot provide progress on human–automation co-ordination. Quantitative ‘who does what’ allocation does not work because the real effects of automation are qualitative: it transforms human practice and forces people to adapt their skills and routines. Rather than re-inventing or refining substitution-based methods, we propose that the more pressing question on human–automation co-ordination is ‘How do we make them get along together?’ Correspondence and offprint requests to: S. W. A. Dekker, Department of Mechanical Engineering, IKP, Link?ping Institute of Technology, SE - 581 83 Link?ping, Sweden. Tel.: +46 13 281646; fax +4613282579; email: sidde@ikp.liu.se     

On BAN logic and hash functions or: how an unjustified inference rule causes problems

BAN logic, an epistemic logic for analyzing security protocols, contains an unjustifiable inference rule. The inference rule assumes that possession of H(X) (i.e., the cryptographic hash value of X) counts as a proof of possession of X, which is not the case. As a result, BAN logic exhibits a problematic property, which is similar to unsoundness, but not strictly equivalent to it. We will call this property ‘unsoundness’ (with quotes). The property is demonstrated using a specially crafted protocol, the two parrots protocol. The ‘unsoundness’ is proven using the partial semantics which is given for BAN logic. Because of the questionable character of the semantics of BAN logic, we also provide an alternative proof of ‘unsoundness’ which we consider more important.     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Unpacking Tasks: The Fusion of New Technology with Instructional Work

This paper discusses how a new technology (designed to help pupils with learning about Shakespeare’s Macbeth) is introduced and integrated into existing classroom practices. It reports on the ways through which teachers and pupils figure out how to use the software as part of their classroom work. Since teaching and learning in classrooms are achieved in and through educational tasks (what teachers instruct pupils to do) the analysis explicates some notable features of a particular task (storyboarding one scene from the play). It is shown that both ‘setting the task’ and ‘following the task’ have to be locally and practically accomplished and that tasks can operate as a sense-making device for pupils’ activities. Furthermore, what the task ‘is’, is not entirely established through the teacher’s initial formulation, but progressively clarified through pupils’ subsequent work, and in turn ratified by the teacher.