Nikolaos Goumagias

Purpose: This paper aims to study the evolution of definitions of internet of things (IoT) through time, critically assess the knowledge these definitions contain and facilitate sensemaking by providing those unfamiliar with IoT with a theoretical definition and an extended framework.Design/methodology/approach: 164 articles published between 2005 and 2019 are collected using snowball sampling. Further, 100 unique definitions are identified in the sample. Definitions are examined using content analysis and applying a theoretical framework of five knowledge dimensions.Findings: In declarative/relational dimensions of knowledge, increasing levels of agreement are observed in the sample. Sources of tautological reasoning are identified. In conditional and causal dimensions, definitions of IoT remain underdeveloped. In the former, potential limitations of IoT related to resource scarcity, privacy and security are overlooked. In the latter, three main loci of agreement are identified.Research limitations/implications: This study does not cover all published definitions of IoT. Some narratives may be omitted by our selection criteria and process.Practical implications: This study supports sensemaking of IoT. Main loci of agreement in definitions of IoT are identified. Avenues for further clarification and consensus are explored. A new framework that can facilitate further investigation and agreement is introduced.Originality/value: This is, to the authors’ knowledge, the first study that examines the historical evolution of definitions of IoT vis-à-vis its technological features. This study introduces an updated framework to critically assess and compare definitions, identify ambiguities and resolve conflicts among different interpretations. The framework can be used to compare past and future definitions and help actors unfamiliar with IoT to make sense of it in a way to reduce adoption costs. It can also support researchers in studying early discussions of IoT.

In this article, we analyse the game play data of three popular customisable card games where players build decks prior to game play. We analyse the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. \myNotes{found} a lack of cross-game analytics while Marchand and Hennig-Thurau identified a lack of understanding of how a game's business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation's impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game's structure and able to compare multiple games. It \myNotes{is applicable to games} that release updates and enables comparison across games. Optimising a game's updates strategy is key as it affects player engagement and retention which directly influence businesses' revenues and profitability in the ${\$}$95 billion global games market.

Designing tax policies that are effective in curbing tax evasion and maximize state revenues requires a rigorous understanding of taxpayer behavior. This work explores the problem of determining the strategy a self-interested, risk-averse tax entity is expected to follow, as it "navigates" - in the context of a Markov Decision Process - a government-controlled tax environment that includes random audits, penalties and occasional tax amnesties. Although simplified versions of this problem have been previously explored, the mere assumption of risk-aversion (as opposed to risk-neutrality) raises the complexity of finding the optimal policy well beyond the reach of analytical techniques. Here, we obtain approximate solutions via a combination of Q-learning and recent advances in Deep Reinforcement Learning. By doing so, we i) determine the tax evasion behavior expected of the taxpayer entity, ii) calculate the degree of risk aversion of the "average" entity given empirical estimates of tax evasion, and iii) evaluate sample tax policies, in terms of expected revenues. Our model can be useful as a testbed for "in-vitro" testing of tax policies, while our results lead to various policy recommendations.

Game Intelligence is knowledge gained by the player or by analysing the data players generate by playing digital games. Serious games for education, raising public awarenesss or changing the players' behaviour are well established and have provided Game Intelligence for decades [1]. However, more recently a trend has begun, inspired by the success of FoldIt [2], [3], of developing games for scientific discovery. These games lower the barrier of entry to complex scientific topics, allowing gamers to contribute to cutting edge research. We argue that this approach is currently underutilized and explore a vision where these games have wider impact. Furthermore, we will discuss the potential of extracting Game Intelligence from games designed originally for entertainment, potentially making all games into scientific discovery games.

We present a Markov-based model of the process via which a ‘representative’ Greek risk-averse firm decides the degree to which it should engage in tax evasion. The model is constructed around a simplified version of the Greek tax system which includes random audits and penalties for under-reporting profits. For its part, the firm is allowed to manipulate its stated profits, potentially exposing itself to future penalty payments, in an attempt to maximize the expected utility of its after-tax wealth. Using our model, we determine the optimal behaviour expected of the firm as a function of the parameters of the tax system, and identify subsets of the audit probability – tax penalty space which ‘remove’ the inventive for tax evasion. This allows us to – among other things – evaluate the effectiveness of the parameter values currently in use and determine the implied level of risk-aversion for the average Greek firm.