Choice architectures and the mobile information revolution. Thomas1, A.M., Parkinson1, J. ... Social media has largely evolved as the significant means of choice ...
2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
Nudging Through Technology Choice architectures and the mobile information revolution Thomas1, A.M., Parkinson1, J., Moore2, P., Goodman1, A., Xhafa3, F. and Barolli4, L. 1
Wales Centre for Behaviour Change, University of Bangor, Bangor, UK. 2 School of Computing, Birmingham City University, Birmingham, UK. 3 Department of Software, Technical University of Catalonia, Barcelona, Spain. 4 Department of Information and Communication Engineering. Fukuoka Institute of Technology, Japan.
result in greater ownership [6]. Whether either view can be fully defended is a philosophical matter, but in terms of application of nudging to the world-wide-web it is likely that the multiplicity of choices, and the ethos of freedom and openness, is likely often to dictate an emphasis on behaviourinfluencing over law-making. Essentially, nudging is based on observations that decisions can be impulsive, irrational, and often based on limited, even lacking, domain knowledge [2]. It therefore relies largely on choice-architecture in an attempt to signpost ethical and rational routes through important decisions [7]. It has been widely applied in domains such as health [8], agriculture [9], education [10], transport and climate change [11]. It is possible that it can often be considered empirical in application, leading to the opinion that research is needed to establish causality and probability links between nudge and achieved behaviour change [12]. So also, its efficacy can be based on whether behaviour change occurs in those who should benefit most from its application (e.g. [11]).
Abstract— The information revolution has radically changed the way we make decisions and has expanded into mobile devices capable of informing choices at the point at which they are made. However, it is arguable whether the explosion of available information helps or hinders decision making, including in important domains such as healthcare and sustainability. Improving on that is the domain of choice architecture, intended to provide decision-knowledge through understanding choiceimplications and so guiding actions in effective and ethical directions (i.e. 'nudging'). Social media has largely evolved as the significant means of choice architecture, but the question remains as to whether decisions based on it are truly effective and ethical, or attracted toward social norms that reinforce poor judgment. This paper explores those issues, including how technologies can be shaped to improve decision-making, and how academic models can be used to nudge toward improved norms. Keywords—component; Choice technology, cloud systems, big data.
I.
architecture,
nudge,
For the purposes of this paper, technological-nudging can be defined as designing computer-systems that augment human decision-making through machine-knowledge and domainmatching, particularly through mobile-device interfaces. It is apparent that web-systems already contain aspects of interest, such as expert knowledge websites, forums and ranking systems (e.g. star-systems, 'like' and '+1'). However, there is limited evidence that these lead to improved behaviour rather than reinforcing established norms (a criticism which can also be applied to poorly-designed nudging [2]). For instance, despite the success of the CrowdMed website, it has been said that increased expert input would be beneficial [13]. It is therefore arguable whether web-systems nudge toward ethical and efficacious decisions, or simply cater for a lust to satisfy impulsive and irrational behaviour through 'closure'. The answer is important: libertarianism should dictate our right to 'good' decisions, rather than simply decisions 'we can live with'. So, it is instructive to consider how technology can be designed around the principles of libertarian paternalism, and Figure 1 provides a conceptual model (around which this paper is organised). The human decision-making cloud system (Section II) represents current online systems, the opinions (expert and otherwise) providing the main information sources, with mediation through, for instance, ranking systems.
INTRODUCTION
Few would argue that modern life is full of decisions, many of which impact our health, our quality of life and even the future of Planet Earth. Opting for the most ethical and rational decisions is difficult, and often leads to a mismatch between what we say we should do, and what we actually do. This ‘value-action gap’ has led to significant interest in 'nudging' techniques (see e.g. [1]). Nudging, in this context, is often known as libertarian (or soft) paternalism: paternal in attempting to guide our choices, and libertarian in defending our freedom to make them, even defending our right to opt out if we wish [2]. The libertarian aspect can be seen to mediate paternalism: use of 'strong graphics' and manipulation of feelings may be paternal, but can often lead to loss of choice [3]. Nudging is predicated on the assumption that choices are often made under circumstances of limited rationality and/or awareness, and can be guided through the way in which the possible choices are presented [4]. It is not, therefore, a form of ‘subtle manipulation’, as manipulation is seldom subtle, and has the potential to alienate decision makers. Therefore, nudging can be contrasted with regulatory routes to behaviour change: in healthcare it has been argued that nudging is not a universal solution, legislation being the preferential option [5]. However, it has also been said that regulation can cause negative attitudes toward issues it addresses, while conversely voluntary behaviour change can 978-0-7695-5094-7/13 $31.00 © 2013 IEEE DOI 10.1109/3PGCIC.2013.44
The machine decision-making cloud system (Section III) is included as a fully-technological means of augmenting human decisions. In many ways this has partially been present since 264 255
the genesis of the web, however, much more can be done to facilitate use of data in decision making, especially given the advances in areas such as massively-parallel computing, artificial intelligence and the cloud. Therefore, data-mining of big-data, development of new knowledge bases, and dynamic creation of data from sensor feeds, can be considered critical elements.
models, but is extended to describe a model within which decision-makers can help fund specific knowledge creation, and decision-helpers can benefit from incrementally small education collation: crowd-funded micro-education. However, despite the seeming simplicity of Figure 1, it should be noted that the millions of users, together with a multitude of servers, sensors, devices and connections, are likely to make it a highly complex system in full operation. Therefore, ‘good’ decisions could be considered emergent properties in a ‘sea of chaos’, potentially forming strange attractors around which associated norms develop. This paper therefore attempts to discuss the technologies that could be used to shape the chaotic behaviour toward the values of libertarian paternalism. Finally, Section V will provide a brief discussion on how this paper fits into the concept of cyberspace: an important component in integration of choicearchitecture and nudging into daily digital-life.
From an academic perspective, academic research will be considered a useful source of domain-knowledge in this system. The role of (academic) education is considered also, in terms of domain-specific expertise creation, validation and use in knowledge-to-decision mapping, within an education cloud (Section IV). It is hypothesised that decision-makers will be influenced more in their behaviour by opinions provided by members of crowds who have not just demonstrable and relevant domain-expertise, but also validated success in its application. This obviously draws heavily on higher education
Fig. 1. A conceptual system layout.
II.
As an example, humans already add to collective intelligence, providing, for example, crowd-sourced crime, weather and traffic data for knowledge-building in the cloud, which can also be combined with machine-knowledge from remote sensing. Despite the usefulness and ubiquity of the term, technically there is no ‘Cloud’, rather there are many clouds (internal to organisational networks, and externally hosted), providing scalability of computing resources and the potential (through wireless networks) for access to significant processing power using mobile devices [14]. The Cloud can be differentiated from the internet through the lack of a need to understand the network, or individual servers, in depth from the outside: storage and processing being allocated, potentially on an ad-hoc and fragmentary basis, when required. It is that scalability that is of obvious importance to nudging for two reasons. Firstly, processing power need not be limited by the number of servers, their speed, nor the number of users
MACHINE KNOWLEDGE
The internet has, since its genesis, been a platform to enhance decision making: as well as hyper-text as a form of knowledge-sharing, aspects such as expert systems have always been evident. That has increased in the decades since the world-wide-web was invented, with current trends in massively-parallel cloud computing providing scalable, efficient and very high speed online computation. For example, Figure 2 shows the Barcelona Supercomputing Center, which is among the most powerful in Europe. Such facilities allow us to transcend, in relative terms, a simple internet, providing opportunities for incorporating concepts such as ‘big-data’, data-mining and virtualization into cloud content. Therefore, the significant artificial-intelligence they provide can be considered an important opportunity for augmenting human decision-making.
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campus at the Open University of Catalonia. As well as academic and administrative activity it incorporates c.50,000 students, with tutors providing lectures for 30 degrees (plus PhD and post-graduate programs) resulting in over 600 official courses. It is completely virtualized with user interaction generating a huge amount (c.10-15GB) of filtered and logged information daily. Processing and analyzing those data supports students and tutors, programme directors, managers and system developers. For instance, tutors can use it for monitoring and to identify dropout rates. Also, programme directors and managers can evaluate virtual class-performance and decide how to improve course/degree organization. Developers can then use the data to identify usage and resource information (e.g. server loads and response times) to optimize performance in the virtual campus.
connected: it is scaled to meet demand. Secondly, through sharing of resources within the cloud it is not necessary to have idle servers during low demand, reducing the environmental impact of computer-based nudging. In terms of cloud-based knowledge, we may loosely categorise it into ‘known’ and ‘unknown’ knowledge. Known knowledge can be considered data and information already collated, which can be incorporated into knowledge-bases (often just simple databases) for access and querying on demand, including through context-matching, expert systems, neural networks, artificial intelligence systems (including robot-connected), or simply through human reading of dynamic web-pages. Despite the obvious parallel to human knowledge, it could also include tacit knowledge, where codified into machine-understandable form. For instance, behaviour data arising from tacit knowledge, once correlated with other apparently unrelated environmental data, can be used predictively. In so doing, tacit knowledge is effectively codified in the statistical relationship between the two data sets. However, the unknown knowledge illustrates a significant aspect of massively-parallel computing: data-mining of bigdata can provide important new knowledge (and continual refinement and update of it) that could be of significant use to decision makers.
A more recent aspect of cloud systems that must also be considered is the Internet-of-Things or, in modern contexts, more correctly the Cloud-of-Things. The current interest in sensors and wireless-sensor networks seeks largely to create ‘big-data’ from millions of internet-addressable (i.e. IPv6) devices, attached to anything from industrial machinery to environmental sensors (i.e. physical ’things’ whose data history is cloud accessible). This allows for significant enhancements to machine-learning systems, often through data-mining of the ‘big-data’ they create. It also allows improvements to personalized service provision, with an individual’s profile (i.e. context) updated to reflect their prevailing state (e.g. emotions or medical symptoms). Therefore, the context-description moves from being simply an account of recent history, to a dynamic representation of prevailing state and behaviour. So, it becomes a more complex, richer and domain-specific representation (see [17] for a fuller discussion). For instance, it has been shown that electricity consumption, measured over short timescales, can provide occupant-behaviour data in homes [18], which is of interest in many areas including clinical home-care and energy-management. It can be measured, to provide data for nudge-design, through smart-metering, but for most contexts more adequate data can easily be obtained through simple, inexpensive, sensors (e.g. [19]), that can be incorporated simply into wireless systems. Similarly, smart-phones can be used in many ways to inform behaviour and for intervention (see e.g. [20] for a wide range of healthcare examples). As an example, Figure 3 shows prototype Android software that measures hand-trembling, in terms of time and frequency domains (both domains being influenced by age and medical conditions).
Fig. 2. Part of the Barcelona Supercomputing Center.
An obvious example is the dynamic context of stock prices, but it is equally appropriate to behaviour based data (e.g. in clinical-care monitoring) and potentially relating previous choices to current decision-scenarios. Given the significant amounts of data generated annually in academia, it is also apparent that research could be designed to facilitate output of its data in a form more readily used by machine-knowledge systems. As examples, research can provide significant links between medical conditions and environment data [15], as well as links between behaviour in Dementia patients and conditionprogression [16]. It could be argued that such research has value to machine-learning systems in a wider context than simply academic use, implying that further research is needed into how the significant body of academic literature can be made available for wider use in nudge-related decision making.
Such apps have a wide range of uses. For instance, trembling in Parkinsons’ Disease sufferers is known to be related to progression of the condition, but progression can also be linked to behaviour changes. Also, for use in medical crowd-systems, it could provide additional data to further refine diagnosis accuracy. But, as an aspect of estimating ‘emotional-charge’ at the decision location (which can be enhanced further, such as through building galvanic skin response measurement into devices: e.g. [21]), it is likely to have other uses in nudging. Also, integration of the Cloud-ofThings and smart-phone technologies opens up many interesting areas of behaviour-sensing.
A real-life example of knowledge building from data, and its use in supporting decision making, is the large virtual
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we can gain comfort and trust knowing that sellers have a 100% positive feedback (even though feedback is given soon after purchase, and so does not reflect problems that may occur later). However, if we choose a cheaper seller with lower feedback, such as 99.1% over 103362 items, we can calculate that over 9000 buyers were less than positive about their purchase. It is indicative of the problem that very few people would actually carry out this calculation in order to choose a seller. Clicking on another link we can gain further information about recent feedback, and can calculate whether problems occurred within a time-window we consider acceptable. In order to fully risk assess (i.e. taking into account probability and severity) we must then read all of the feedback and apply some form of qualitative analysis, deciding whether the risks outweigh the advantages: generally whether the goods will be as described, arrive in an acceptable timeframe, and that the cost is worth the risk over a more expensive seller with higher feedback. Also, all of the above must be undertaken by the buyer, as auction sites do not generally allow sorting of results based on a perceived-risk algorithm. Under these circumstances, it is apparent that current web-systems do not fully represent decision-descriptions, and do not minimise risk of the ‘wrong’ decision being made. Also, even though the majority of ‘feedback’ is provided by buyers and sellers (i.e. humans) it is apparent that the model does not fully enhance our decision making, especially as it does not tell us whether the purchase is ethical, worthwhile, or necessary. Similarly, ‘liking’ a post, video or product can be argued to reinforce what is popular, rather than what is truly ethical. So, moving norms toward more positive stances requires a means through which users can assess the ethicalities of their ‘likes’, including knowing the efficacy of the norm. For instance, environmental research provides much data on which products and transport can be judged: for example life-cycle analysis (LCA). If such data were included in auction listings, sorted and rated through inclusion of environmental footprints, it could nudge choices toward the more environmentally-positive. Similarly, from a sustainability in computing perspective, it would be interesting to observe the impact of rating video-clips based not just on popularity, but also server/device power consumption (and related CO2 equivalent emissions).
Fig. 3. Prototype hand trembling Android app.
For instance, Figure 4 shows RFID tags (compatible with NFC devices) that can be written to and read by a wide range of smart-phones and tablets. Also shown is an inexpensive RFID reader that can be used with the tags, such as built into furniture, ornaments, consumer-goods and even clothing for wearable sensor systems. The sensor modules shown in Figure 4 include examples for inexpensive temperature, humidity, acceleration, rotation and red-green-blue light measurements. Many other inexpensive sensors are similarly available, and easily web-connected for mobile-device interfacing. However, in terms of using mobile devices for decision-support, it is important to recognise that potential problems such as (environmentally) unsustainable power-consumption due to graphic-intensive apps [22], and compulsive/obsessive mobiledevice use [23], must be properly considered and mitigated where possible.
However, as well as within the cloud, decision scenarios can be augmented using mobile devices, such as smart-phones. As well as providing on-the-spot access to machine and crowd knowledge, through mobile broadband, they also often incorporate a wide range of sensors. They can, for instance, be used for, and interfaced to, physiological (e.g. gait and ECG) and environment (e.g. temperature, pressure, light-level) monitoring (see e.g. [19]). They can also provide locationcontext data for use in decision-support systems. However, they can be used for much more, as demonstrated by the prototype Android app of Figure 5. The system allows interaction with everyday objects through barcodes, RFID (more accurately NFC, which is largely compatible with 13.56MHz RFID tags) and voice-recognition.
Fig. 4. Sensors, RFID tags and an RFID reader.
III.
AUGMENTING HUMAN KNOWLEDGE
As Hertwig and Erev [24] note, decisions can be especially difficult when we have little experience on which to base them, often because we underestimate the risks associated with decisions where there is a significant gap between experience and decision-description. We are also faced with difficult choices when time constraints limit objective analysis, or impulsivity overrides sensibility. In fact, the two may be linked, as we may feel pressured into a decision knowing that trying to analyse the options fully, in the available time, is futile. As an example, when buying goods on an auction site
This has many uses in, for instance, healthcare, particularly for people coping with cognitive challenges such as in Dementia conditions, who can find remembering things
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IV.
challenging. The prototype of Figure 5 can not only provide support through images, text and videos, but can also provide voice direction and interactive graphics. The potential for such systems is obviously significant in other areas where simplified access to information is required. However, it has four specific advantages that should be recognised: it can provide indoor location data (e.g. which rooms have been visited and when), it can provide behaviour data (e.g. has a meal been prepared), it can provide decision context data (e.g. the item being interrogated), and can be interfaced to automated systems (such as in smart-homes and care-spaces).
MICRO-EDUCATION AND COMPETENCY
Many areas of higher education are moving toward virtual content, either to supplement courses on a physical campus, or as the primary route to qualifications (see [26] for full discussion of the virtual campus concept). In the Cloud, this is often achieved through course-management systems (e.g. Moodle), distributed multimedia [27] and virtual laboratories. Virtual laboratories, in particular, are becoming central to online education, having been described as ‘crucial to the future of learning’ [28] and for physical systems importantly allow for learning through repetition [29]. As an example, the virtual laboratory of Figure 7 provides a complete simulation of measurement equipment used in geophysics, including mixed 2D and 3D graphics. Using HTML5 on mobile devices, virtual laboratories can enable learn-anywhere education that goes far beyond simple text, images and video. In general, virtual education is provided as part of complete qualifications, such as undergraduate and postgraduate degrees. However, that need not be so in the context of nudging, as it can be argued that even small, incremental, knowledge gains can nudge people toward improved decision making. In that regard, the Open University (open.ac.uk) is relevant, as it provides modular courses that can be used individually (e.g. for interest or continual professional development), combined to form qualifications (certificates, diplomas and degrees), or studied free of charge through open-access content if no accreditation is required. They also provide small ‘taster’ courses, as well as making significant use of online assessment. It is evident that people paying for such online education realise that it will benefit their knowledge, experience, and decision-making abilities. So, it can also be argued that it is a form of ‘self-nudging’, when applied as domain-specific knowledge (i.e. expert knowledge is often of less use when applied generally).
Fig. 5. Interaction prototype using RFID, barcodes and speech.
As decisions can be impulsive and irrational, it is also possible that mobile devices could be used to assess emotions to enhance decision support. For instance, there is currently much interest in Kansei Engineering as a methodology to deal with human feelings, demands, and impressions in contextaware applications. It is a Japanese term meaning sensibility, impression, and emotion [25] and Kansei words provide adjectives describing human emotion, sensibility and impression. Analysis of texts, voice calls and other interactions therefore provide the opportunity to capture an implicit measure of emotion-state. Furthermore, humans are sensors into their own states, and so explicit ‘state’ data can also be collected through mobile interfaces. For example, the prototype system shown in Figure 6 allows speakers to assess reactions to their presentations in real-time. It could be used to nudge performance in real-time, based on emotional response (as well as accessed later for debriefing and improvement planning). A clear advantage of incorporating both Kansei-like and questionnaire-like data is that they capture very different, yet complimentary, types of psychophysiological data.
Fig. 7. A cloud-based virtual laboratory [26].
Furthermore, education is widely valued as an indicator of ‘good judgement’ and so it can be hypothesised to also be a good indicator of quality in online crowd-mediated decision systems. For instance, the CrowdMed website uses a points system to indicate users with the highest success rates [13], which relies on a posteriori knowledge-creation. Integrating micro-education into the system would allow a priori knowledge integration. That could be considered critical as
Fig. 6. Emotion-based feedback prototype for speakers.
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VI.
prior success does not fully illustrate knowledge-transferability between domains, whereas micro-qualifications can be classified in terms of their applicability to individual choices: i.e. they represent specific-competencies rather than simply knowledge-levels. Micro-education need not, of course, be expensive. It can also be undertaken fully through online content, particularly virtual laboratories, that allow automated content-serving, testing and accreditation. However, it is possible that small donations by decision makers could be used for crowd-funding of micro-modules, with crowd-mediated assignment of funds based on knowledge of current education state, prior success rate and the importance of the study topicarea (i.e. education and decision contexts). V.
CONCLUSIONS
It must be apparent to all readers that limited-rationality in human life often results in poor decision making that impacts on the three pillars of sustainable development: individual, societal, and environmental cost. This may be due to factors such as decision time, impulsivity, domain-specific knowledge, and misunderstanding decision-risks. So, improving decisions could be considered to require filling gaps between decisiondescriptions and user-experience, which has obvious parallels with the ethos of academia. Therefore, a rationale has been proposed for development of technological systems that aid in ensuring decision-makers have access to the knowledge they need for objective, rational, decisions that shape their, and our, futures in a more positive direction.
INTEGRATION INTO CYBERSPACE
For instance, advances in massively-parallel computing, and cloud knowledge-bases, can allow almost-instantaneous access to domain-knowledge and objective advice. In parallel, integration of the cloud-of-things allows us to take advantage of dynamic knowledge of the world around us, including userspecific environment and physiological data. Together with other context data (e.g. location services, RFID, barcodes, and even emotions) it allows significant service-personalization for users, as well as increased, and more relevant, data on which machines and crowds can base their advice.
The increasing complexity of computer systems over recent decades enhances the power of the metaphor of cyberspace, with users existing as cybernauts within it. The core aspects of cyberspace can be argued to have become more prevalent with the advent of Web 2.0: particularly the increased reliance on social media and the (heavily machine and device mediated: see [30]) human-to-human communications options it facilitates. Similarly, it can be argued that ‘the Cloud’ has further developed Cyberspace, making data, applications and social connections not only un-locatable for users, but also largely intangible assets. As users we can navigate it in many ways, including through hypertext and virtual reality, making cyberspace a ‘place’ where virtual existence can be anything from superficial to fully-immersive.
However, the potential for academia to contribute to such an undertaking must not be underestimated. For instance, research can be considered the generation of knowledge to benefit human-kind. Massive amounts of data are generated annually in academia, and could be used as the basis for knowledge-bases for improved decision-making. Furthermore, a central goal of education is to provide skills, knowledge and experience that can be abstracted by learners to new decisionscenarios. It therefore explicitly seeks to enhance decisionmaking: to minimise risk and maximise ethical, technological and socially-acceptable outcomes.
Cyberspace is also now a place populated not just with human decision makers: for instance, intelligent decision-based systems and agents now abound. That change largely relies on data-mining of big-data, with many businesses now reliant on software to analyse-and-decide, such as in financial markets where much trading is now automated. The size of datasets (potentially many petabytes) in big-data analysis, and the speed at which data can be added, dictate that human involvement will become increasingly less demanding into the future. Therefore, there is no reason why the power of big-data should not be proposed as a means of augmenting human decision making by providing, and framing, decision-related information that facilitates ethical and life-improving decision outcomes.
Therefore, micro-education has been proposed as a means of enhancing crowd involvement, potentially including crowdfunded learning opportunities to ‘micro-nudge’ crowdknowledge toward objective and ethical social norms. So, in conclusion, this paper does not argue that technology can be considered a tool for use in nudging, rather it argues that a well developed system integrating individual users, crowds, webcontent, machine-knowledge, micro-education and the cloudof-things, provides an opportunity to augment humans such that they can make the efficacious and ethical decisions they often desire. In so doing, cyberspace may become both paternal and libertarian, supporting a positive and ethical future for humankind.
When based on big-data that allows humans to make decisions that are data-aware, allowing them to leverage the power of big-data analysis that is as dynamic over time as their needs, and even based on almost-real-time sensing (e.g. the web-of-things). Therefore, the systems described herein are not entirely new, being an extension of the use of data-aware agents and systems in business to application for humans in their daily lives. So, our research around nudging and technology is being based on the need not to create cyberspace anew, but allowing its design to evolve as a means of supporting environmentally-sustainable and life-improving choices. Therefore, integrating machine-knowledge, humanknowledge and micro-education can be seen as a means of attempting to ensure cyberspace can be considered both paternal and libertarian.
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