Designing the past: the National Trust as a social-material agency

The National Trust was founded in 1895 for ‘the preservation of places of historic interest or natural beauty’. While the distinction between the cultural and the natural seemed obvious at that time and members and visitors were not even implicated actors, we argue that the National Trust may be better understood as a co-constructed network effect of the social and material, which in turn affords social-material agency. There are currently 3.5 million members of the National Trust and 50 million visitors every year to National Trust properties, which include the largest collection of gardens in the world and over 300 historic houses and open-air properties. While the notion of design itself may seem to be an exemplar of the humanist love of agency, we argue (following Latour) that traditional notions of agency, which were asymmetrically distributed to the human actors, take insufficient cognisance of evident occasions of ‘material agency’ (Pickering, 1995) and the site of conservation is one site whereby the agency produced by social-material assemblages seems interesting and revealing. Whereas the social-material practices of design may seem in some tension with those of conservation, we argue in this paper that a close analysis of a particular site of conservation shows a manifold of ‘designing’ actors. Whatever the National Trust conserves could be considered as an example of particular and situated designs condensed from the interactions of humankind and nature. Similarly the visitor experience is also designed. While conservation can imply a certain social-material agency, it is much less well understood how conservation co-produces agency, and how these network effects serve the purposes of conservation by the Trust, visitors and other actors through the agency of the social and material. This paper will reveal some of the social-material practices which afford a visit to a property and what such visits afford the social-material practices of the National Trust

Protocols for distributive scheduling

The increasing complexity of space operations and the inclusion of interorganizational and international groups in the planning and control of space missions lead to requirements for greater communication, coordination, and cooperation among mission schedulers. These schedulers must jointly allocate scarce shared resources among the various operational and mission oriented activities while adhering to all constraints. This scheduling environment is complicated by such factors as the presence of varying perspectives and conflicting objectives among the schedulers, the need for different schedulers to work in parallel, and limited communication among schedulers. Smooth interaction among schedulers requires the use of protocols that govern such issues as resource sharing, authority to update the schedule, and communication of updates. This paper addresses the development and characteristics of such protocols and their use in a distributed scheduling environment that incorporates computer-aided scheduling tools. An example problem is drawn from the domain of space shuttle mission planning

Calibration, validation and the NERC Airborne Remote Sensing Facility

The application of airborne and satellite remote sensing to terrestrial applications has been dominated by empirically-based, semi-quantitative approaches, in contrast to those developed in the marine and atmospheric sciences which have often developed from rigorous physically-based models. Furthermore, the traceability of EO data and the methodological basis of many applications has often been taken for granted, with the result that the repeatability of analyses and the reliability of many terrestrial EO products can be questioned. ‘NCAVEO’ is a recently established network of Earth Observation experts and data users committed to exchanging knowledge and understanding in the area of remote sensing data calibration and validation. It aims to provide a UK-based forum to collate available knowledge and expertise associated with the calibration and validation of EO-based products from both UK and overseas providers, in different discipline areas including land, ocean and atmosphere. This paper will introduce NCAVEO and highlight some of the contributions it hopes to make to airborne remote sensing in the UK

Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation

The US mobile phone service industry has dramatically consolidated over the last two decades. One justification for consolidation is that merged firms can provide consumers with larger coverage areas at lower costs. We estimate the willingness to pay for national coverage to evaluate this motivation for past consolidation. As market level quantity data is not publicly available, we devise an econometric procedure that allows us to estimate the willingness to pay using market share ranks collected from a popular online retailer, Amazon. Our semiparametric maximum score estimator controls for consumers' heterogeneous preferences for carriers, handsets and minutes of calling time. We find that national coverage is strongly valued by consumers, providing an efficiency justification for across-market mergers. The methods we propose can estimate demand for other products using data from Amazon or other online retailers where quantities are not observed, but product ranks are observed. Since Amazon data can easily be gathered by researchers, these methods may be useful for the analysis of other product markets where high quality data are not publicly available.Technology and Industry

Multiscale free energy analysis of human ecosystem engineering

Unlike ecosystem engineering by other living things, which brings a relatively limited range of sensations that are connected to a few enduring survival preferences, human ecosystem engineering brings an increasing variety and frequency of novel sensations. Many of these novel sensations can quickly become preferences as they indicate that human life will be less strenuous and more stimulating. Furthermore, they can soon become addictive. By contrast, unwanted surprise from these novel sensations may become apparent decades later. This recognition can come after the survival of millions of humans and other species has been undermined. In this paper, it is explained that, while multiscale free energy provides a useful hypothesis for framing human ecosystem engineering, disconnects between preferences and survival from human ecosystem engineering limit the application of current assumptions that underlie continuous state-space and discrete state-space modelling of active inference

Psychomotor Predictive Processing

Psychomotor experience can be based on what people predict they will experience, rather than on sensory inputs. It has been argued that disconnects between human experience and sensory inputs can be addressed better through further development of predictive processing theory. In this paper, the scope of predictive processing theory is extended through three developments. First, by going beyond previous studies that have encompassed embodied cognition but have not addressed some fundamental aspects of psychomotor functioning. Second, by proposing a scientific basis for explaining predictive processing that spans objective neuroscience and subjective experience. Third, by providing an explanation of predictive processing that can be incorporated into the planning and operation of systems involving robots and other new technologies. This is necessary because such systems are becoming increasingly common and move us farther away from the hunter-gatherer lifestyles within which our psychomotor functioning evolved. For example, beliefs that workplace robots are threatening can generate anxiety, while wearing hardware, such as augmented reality headsets and exoskeletons, can impede the natural functioning of psychomotor systems. The primary contribution of the paper is the introduction of a new formulation of hierarchical predictive processing that is focused on psychomotor functioning

Human-artificial intelligence systems: How human survival first principles influence machine learning world models

World models is a construct that is used to represent internal models of the world. It is an important construct for human-artificial intelligence systems, because both natural and artificial agents can have world models. The term, natural agents, encompasses individual people and human organizations. Many human organizations apply artificial agents that include machine learning. In this paper, it is explained how human survival first principles of interactions between energy and entropy influence organization’s world models, and hence their implementations of machine learning. First, the world models construct is related to human organizations. This is done in terms of the construct’s origins in psychology theory-building during the 1930s through its applications in systems science during the 1970s to its recent applications in computational neuroscience. Second, it is explained how human survival first principles of interactions between energy and entropy influence organizational world models. Third, a practical example is provided of how survival first principles lead to opposing organizational world models. Fourth, it is explained how opposing organizational world models can constrain applications of machine learning. Overall, the paper highlights the influence of interactions between energy and entropy on organizations’ applications of machine learning. In doing so, profound challenges are revealed for human-artificial intelligence systems

Accessing active inference theory through its implicit and deliberative practice in human organizations

Active inference theory (AIT) is a corollary of the free-energy principle, which formalizes cognition of living system’s autopoietic organization. AIT comprises specialist terminology and mathematics used in theoretical neurobiology. Yet, active inference is common practice in human organizations, such as private companies, public institutions, and not-for-profits. Active inference encompasses three interrelated types of actions, which are carried out to minimize uncertainty about how organizations will survive. The three types of action are updating work beliefs, shifting work attention, and/or changing how work is performed. Accordingly, an alternative starting point for grasping active inference, rather than trying to understand AIT specialist terminology and mathematics, is to reflect upon lived experience. In other words, grasping active inference through au-toethnographic research. In this short communication paper, accessing AIT through autoethnography is explained in terms of active inference in existing organizational practice (implicit active inference), new organizational methodologies that are informed by AIT (deliberative active inference), and combining implicit and deliberative active inference. In addition, these autoethnographic options for grasping AIT are related to generative learning.</p