Deceptive Problems Research Articles

Mission Planning and Trajectory Optimization in UAV Swarm for Track Deception against Radar Network

In this article, a mission planning and trajectory optimization scheme in unmanned aerial vehicle (UAV) swarm for track deception against radar networks is proposed. The core of this scheme is to formulate the track deception problem as a model with the objective of simultaneously maximizing the number of phantom tracks while minimizing the total flight distance of the UAV swarm, subject to the constraints of UAV kinematic performance, phantom track rotation angles, and a homology test. It is shown that the formulated track deception problem is a mixed-integer programming, multivariable, and non-linear optimization model. By incorporating mission planning based on platform reuse and a particle swarm optimization (PSO) algorithm, a three-stage solution methodology is proposed to tackle the above problem. Through joint optimization for mission planning and flight trajectories of the UAV swarm, a low-speed UAV swarm is capable of generating a number of high-speed phantom tracks. Numerical results demonstrate that the proposed scheme enables a low-speed UAV swarm to generate as many high-speed phantom tracks as possible, effectively achieving track deception against radar network.

Runtime Analysis of Quality Diversity Algorithms

AbstractQuality diversity (QD) is a branch of evolutionary computation that gained increasing interest in recent years. The Map-Elites QD approach defines a feature space, i.e., a partition of the search space, and stores the best solution for each cell of this space. We study a simple QD algorithm in the context of pseudo-Boolean optimisation on the “number of ones” feature space, where the ith cell stores the best solution amongst those with a number of ones in $$[(i-1)k, ik-1]$$ [ ( i - 1 ) k , i k - 1 ] . Here k is a granularity parameter $$1 \le k \le n+1$$ 1 ≤ k ≤ n + 1 . We give a tight bound on the expected time until all cells are covered for arbitrary fitness functions and for all k and analyse the expected optimisation time of QD on OneMax and other problems whose structure aligns favourably with the feature space. On combinatorial problems we show that QD finds a $${(1-1/e)}$$ ( 1 - 1 / e ) -approximation when maximising any monotone sub-modular function with a single uniform cardinality constraint efficiently. Defining the feature space as the number of connected components of an edge-weighted graph, we show that QD finds a minimum spanning forest in expected polynomial time. We further consider QD’s performance on classes of transformed functions in which the feature space is not well aligned with the problem. The asymptotic performance is unaffected by transformations on easy functions like OneMax. Applying a worst-case transformation to a deceptive problem increases the expected optimisation time from $$O(n^2 \log n)$$ O ( n 2 log n ) to an exponential time. However, QD is still faster than a (1+1) EA by an exponential factor.

Objective acceptability indexes

Based on the financial efficiency and leverage effect, this study classifies the measures of reward, risk, and performance into “simple” and “compound” types. We generalize three problems of observation inconsistency, overleverage deception, and dynamic manipulation behind classical simple measures (such as variability, LPM, VaR, and regression-based measures), and two problems of observation inconsistency and leverage variance behind traditional drawdown-based compound measures. We employ an axiomatic approach to answer the question of what undesirable influences measures should avoid, and complement the axiomatic framework with objective acceptability indexes by separating useless influences that do not add value for investors. We also develop new compound measures to solve the general problems with empirical evidence.

Community Deception in Large Networks: Through the Lens of Laplacian Spectrum

Many complex networks in the real world have community structures. Typical examples include online social networks and ecology networks. While the identification of communities bears numerous practical applications, with the increasing awareness of data security and privacy concerns, the need to protect the community affiliations of individuals from disclosing by attackers emerges. This raises the community deception (CD) problem, that is, the opposite of community detection, which asks for ways to minimally perturb the network structures by rewiring nodes so that the target communities maximally hide themself from community detection algorithms. To this end, we investigate the CD problem through a Laplacian spectrum lens and propose a method named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> to hide a flexible target set of communities, which is more universal than most existing methods that either focus on hiding the entire communities or a single community. The key idea of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> is to first allocate the resources of perturbations fairly and effectively. By proving that hiding communities through intercommunity edge addition and intracommunity edge deletion correspond to maximizing the second smallest eigenvalue <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda_2$</tex-math> </inline-formula> and minimizing the largest eigenvalue <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda_n$</tex-math> </inline-formula> of the graph Laplacian, respectively, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> then incorporates efficient heuristics for approximately solving the problems, thus selecting the appropriate edge to perturb. Experimental results over nine real-world networks and six community detection algorithms not only demonstrate the efficiency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> , but also the superior performance on obfuscating community structures over the baselines.

Artificial Sociality

This article proposes the notion of Artificial Sociality to describe communicative AI technologies that create the impression of social behavior. Existing tools that activate Artificial Sociality include, among others, Large Language Models (LLMs) such as ChatGPT, voice assistants, virtual influencers, socialbots and companion chatbots such as Replika. The article highlights three key issues that are likely to shape present and future debates about these technologies, as well as design practices and regulation efforts: the modelling of human sociality that foregrounds it, the problem of deception and the issue of control from the part of the users. Ethical, social and cultural implications are discussed that are likely to shape future applications and regulation efforts for these technologies.

Criticizing Danaher’s Approach to Superficial State Deception

If existing or future robots appear to have some capacity, state or property, how can we determine whether they truly have it or whether we are deceived into believing so? John Danaher addresses this question by formulating his approach to what he refers to as superficial state deception (SSD) from the perspective of his theory termed ethical behaviourism (EB), which was initially designed to determine the moral status of robots. In summary, Danaher believes that focusing on behaviour is sufficient to determine whether SSD occurs. My general claim is that Danaher’s approach to SSD based on EB is implausible since it results in the impossibility of conceptualizing SSD, e.g., it does not enable determining whether or not SSD occurs in a particular case. Moreover, I show how Danaher’s approach to SSD needs to be transformed to become plausible. To make my point, I (1) examine the main features of EB and distinguish its two versions by showing how Danaher revised the original EB in response to criticism; (2) discuss Danaher’s approach to the problem of deception from the perspective of EB; (3) criticize that approach by showing that it requires revisions analogous to those that have already been recommended in reference to EB, and (4) propose an alternative method for determining the presence of SSD that covers diverse, plausible approaches to SSD.

The Problem of Clinical Deception and Why We Cannot Begin in the Middle

In this brief commentary, I offer an appreciative yet critical analysis of Abram Brummett and Erica Salter's article, "Mapping the Moral Terrain of Clinical Deception." I challenge the authors to clarify their choice of the term "deception" (as opposed to "lying" or "dishonesty"), and I explain how these different terms may affect one's moral analysis. I also draw attention to the authors' claim that veracity is the ethical default of clinicians. I argue that their failure to defend this claim renders their framework more limited in its usefulness than they seem to acknowledge. While their framework does an excellent job of identifying morally salient features of clinical deception, it cannot be used to measure the strength of justification for an act of deception apart from a normative conception of truthfulness.

Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks.

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster’s trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity.

Honeypot Allocation for Cyber Deception Under Uncertainty

Cyber deception aims to misrepresent the state of the network to mislead the attackers, falsify their reconnaissance conclusions, and deflect them away from their goals. Honeypots serve as decoy devices inside networks that can capture adversaries for monitoring purposes. We propose a two-phase deception approach based on honeypot allocation. In the first phase, we develop a proactive deceptive honeypot allocation policy, the second phase proposes a reactive deception approach that dynamically allocates honeypots according to IDS updates. Considering a practical scenario, the defender partially monitors the adversary’s activities. To this end, we develop our deception approach using a combination of game-theoretic and reinforcement learning models. We cast the problem of reactive deception as a partially observable Markov decision process (POMDP) based on a game-theoretic dynamic model to accommodate the imperfect monitoring of the actions taken by the attacker. We solve this combined partially observable game model using Monte-Carlo tree search to overcome the game model complexity. We give a game-theoretic analysis to explain the attack-defense policies at equilibrium. Finally, we present numerical results to validate the effectiveness of the proposed deception approach.

Community deception: from undirected to directed networks

Community deception is about hiding a target community that wants to remain below the radar of community detection algorithms. The goal is to devise algorithms that, given a maximum number of updates (e.g., edge additions and removal), strive to find the best way to perform such updates in order to hide the target community inside the community structure found by a detection algorithm. So far, community deception has only been studied for undirected networks, although many real-world networks (e.g., Twitter) are directed. One way to overcome this problem would be to treat the network as undirected. However, this approach discards potentially helpful information in the edge directions (e.g., A follows B does not imply that B follows A). The aim of this paper is threefold. First, to give an account of the state-of-the-art community deception techniques in undirected networks underlying their peculiarities. Second, to investigate the community deception problem in directed networks and to show how deception techniques proposed for undirected networks should be modified and adapted to work on directed networks. Third, to evaluate deception techniques both in undirected and directed networks. Our experimental evaluation on a variety of (large) directed networks shows that techniques that work well for undirected networks fail short when directly applied to directed networks, thus underlying the need for specific approaches.

Augmented Reality, Augmented Epistemology, and the Real-World Web

Augmented reality (AR) technologies function to ‘augment’ normal perception by superimposing virtual objects onto an agent’s visual field. The philosophy of augmented reality is a small but growing subfield within the philosophy of technology. Existing work in this subfield includes research on the phenomenology of augmented experiences, the metaphysics of virtual objects, and different ethical issues associated with AR systems, including (but not limited to) issues of privacy, property rights, ownership, trust, and informed consent. This paper addresses some epistemological issues posed by AR systems. I focus on a near-future version of AR technology called the Real-World Web, which promises to radically transform the nature of our relationship to digital information by mixing the virtual with the physical. I argue that the Real-World Web (RWW) threatens to exacerbate three existing epistemic problems in the digital age: the problem of digital distraction, the problem of digital deception, and the problem of digital divergence. The RWW is poised to present new versions of these problems in the form of what I call the augmented attention economy, augmented skepticism, and the problem of other augmented minds. The paper draws on a range of empirical research on AR and offers a phenomenological analysis of virtual objects as perceptual affordances to help ground and guide the speculative nature of the discussion. It also considers a few policy-based and designed-based proposals to mitigate the epistemic threats posed by AR technology.

Cosimo I’s Self‐Made Spy: Information and Misinformation in Sixteenth‐Century Venice and Florence

AbstractDrawing on newly discovered archival documents, the article unearths the story of Antonio Beghini, a self‐made spy operating in Venice for Cosimo I de’ Medici duke of Florence in the mid‐sixteenth century. In 1542 Beghini revealed a sensational coup d’état planned by Florentine exiles and French against the Republic of Venice. This was the starting point for a series of vicissitudes that included two imprisonments and encounters with important rulers and diplomats. Beghini’s story allows us to gain a deeper understanding of the murkiest side of Cosimo’s information network and of the troubled community of the Florentine exiles in Venice. This story is also an opportunity to reflect on the problem of misinformation, unreliability, and deception in sixteenth‐century Florence and Venice, at a time when the boundary between true and false was much blurrier than it is today.

The Role of Deception in a Game of ‘Hide and Seek’

This paper introduces the problem of active deception into the literature on search theory. We consider a game of ‘hide and seek’ in which a hider chooses to hide in one of many cells. Our hider also wishes to remain active which runs the risk of signaling his location. The searcher can look in any cell within the hider’s feasible operating environment but must prioritize his search. To do so, he looks for and has the option of following any indicator of the hider’s possible whereabouts. In conducting his search, however, he also risks signaling where he is about to look next. This gives the hider an opportunity to evade. Each player can employ ‘deceptive signals’, at some cost, to improve their respective chances of success. We examine the nature of the tradeoffs involved in deciding whether or not to use deceptive tactics under different assumptions about the nature of the players’ common operating environment.

Exploiting Partial Observability for Optimal Deception

Deception is a useful tool in situations where an agent operates in the presence of its adversaries. We consider a setting where a supervisor provides a reference policy to an agent, expects the agent to operate in an environment by following the reference policy, and partially observes the agent's behavior. The agent instead follows a different, deceptive policy to achieve a different task. We model the environment with a Markov decision process and study the synthesis of optimal deceptive policies under partial observability. We formalize the notion of deception as a hypothesis testing problem and show that the synthesis of optimal deceptive policies is NP-hard. As an approximation, we consider the class of mixture policies, which provides a convex optimization formulation of the deception problem. We give an algorithm that converges to the optimal mixture policy. We also consider a special class of Markov decision processes where the transition and observation functions are deterministic. For this case, we give a randomized algorithm for path planning that generates a path for the agent in polynomial time and achieves the optimal value for the considered objective function.

The practice of technological deception in videoconferencing systems for distance learning and ways to counter it

This article raises the problem of high-tech deception using video conferencing means during distance learning, which is of increased relevance due to the digitalization of the educational process, with the growth of digital literacy of young people. The article presents some methods of fraud, including a relatively new technology that is very popular: Deepfake. The article details two popular tools for replacing faces, each of which provides step-by-step instructions on how to create, configure and apply in life. The organizational methods that are presented will help teachers detect even the most disguised forgery, including future voice spoofing, and stop any attempt at deception. At the end of the article, the system is described with the help of the technologies that used to combat deepfakes, and an assessment of the danger of existing tools is given. The objectives of this article are to raise public interest in the problem of face substitution and high-tech deception in general, to create grounds for discussing the need to switch to a remote format of events, to broaden the horizons of the reader and provide him with an area for further work and research.

Should Mistaken Consent Still Be Consent? In Defence of an Incremental Understanding of Consent in the Sexual Offences Act 2003

The article considers the recent case of R v Lawrance [2020] EWCA Crim 971 and the way in which the courts are deciding to interpret the concept of freedom under s. 74 of the Sexual Offences Act 2003. It is argued that the case shines a light upon the lacuna present in the Act with regards to deception and consent, identified by academics when the Act was promulgated. It is suggested that the definition of consent lacks clarity as courts have struggled in its application. The resulting ‘so closely connected’ test developed in the cases of R (on the application of Monica) v DPP [2018] EWHC 3508 (Admin); [2019] QB 1019 and R v Lawrance [2020] EWCA Crim 971 is not an adequate solution to the problem of deception and consent and potentially muddies the waters further. The article offers a potential solution that might better uphold the ambition of protecting sexual autonomy while adhering to the concept of fair-labelling.

Hide and Seek: Outwitting Community Detection Algorithms

Community affiliation of a node plays an important role in determining its contextual position in the network, which may raise privacy concerns when a sensitive node wants to hide its identity in a network. Oftentimes, a target community seeks to protect itself from adversaries so that its constituent members remain hidden inside the network. The current study focuses on hiding such sensitive communities so that the community affiliation of the targeted nodes can be concealed. This leads to the problem of community deception, which investigates the avenues of minimally rewiring nodes in a network so that a given target community maximally hides from a community detection algorithm (CDA). We formalize the problem of community deception and introduce Network deception using permanence loss (NEURAL), a novel method that greedily optimizes a node-centric objective function to determine the rewiring strategy. Theoretical settings pose a restriction on the number of strategies that can be employed to optimize the objective function, which in turn reduces the overhead of choosing the best strategy from multiple options. We also show that our objective function is submodular and monotone. When tested on both synthetic and seven real-world networks, NEURAL is able to deceive six widely used CDAs. We benchmark its performance with respect to four state-of-the-art methods on four evaluation metrics. In addition, our qualitative analysis on three other attributed real-world networks reveals that NEURAL, quite strikingly, captures important metainformation about edges that otherwise could not be inferred by observing only their topological structures.

Weight-Adapted Convolution Neural Network for Facial Expression Recognition in Human–Robot Interaction

The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network’s (CNN’s) potential performance in avoiding local optima and speeding up convergence by the hybrid genetic algorithm (HGA) with optimal initial population, in such a way that it realizes deep and global emotion understanding in human–robot interaction. Moreover, the idea of novelty search is introduced to solve the deception problem in the HGA, which can expend the search space to help genetic algorithm jump out of local optimum and optimize large-scale parameters. In the proposal, the facial expression image preprocessing is conducted first, then the low-level expression features are extracted by using a principal component analysis. Finally, the high-level expression semantic features are extracted and recognized by WACNN which is optimized by HGA. In order to evaluate the effectiveness of WACNN, experiments on JAFFE, CK+, and static facial expressions in the wild 2.0 databases are carried out by using ${k}$ -fold cross validation, and experimental results show the recognition accuracies of the proposal are superior to that of the state-of-the-art, such as local directional ternary pattern and weighted mixture deep neural network (DNN), which aim to extract discriminative and are the DNN-based methods. Moreover, recognition accuracies of the proposal are also higher than the deep CNN without HGA, which indicates that the proposal has better global optimization ability. Meanwhile, preliminary application experiments are also carried out by using the proposed algorithm on the emotional social robot system, where nine volunteers and two-wheeled robots experience the scenario of emotion understanding. Application results indicate that the wheeled robots can recognize basic expressions, such as happy, surprise, and so on.

Liar, Liar, Pants On Fire: How Deception Is Burning The Criminal Justice System

Spoken from the lips of an officer, a little white lie, like a little red match, can create an uncontrollable fire with the power to incinerate the criminal justice system. For decades, police deception has been a pervasive component of interrogations and this has led to high rates of false confessions, public distrust of the police, and has undermined the police as a moral authority. As a result of this police deception, not only are more innocents wrongfully convicted, but the criminal justice system becomes less effective as the public no longer trusts the police enough to cooperate with them or follow their moral path. As such, the Supreme Court has criticized these common practices of police deception, however, it has never prohibited it. This Article argues that not only should police deception in interrogations be prohibited for the harmful outcomes it produces, but it should also be prohibited because it violates the Fifth Amendment as a form of phycological coercion as it psychologically manipulates suspects and overcomes their free will. After discussing recent research and data that portrays the horrendous effects of police deception, I propose a three-part solution. First, the Supreme Court should prohibit police deception in interrogations through a per se rule. Additionally, current insufficient police trainings should be reformed in both quality and quantity to focus on ethics and morality. Lastly, legislators should statutorily require all interrogations to be videotaped from start to finish to deter deception. While each part of this solution has the potential to remedy the harms of police deception on its own, combining the three would ensure a prompt elimination of the flames set by police deception. Although some scholars have addressed the problem of deception in interrogations and proposed their own solutions, none have recognized that putting out the fire consuming our criminal justice system in time will require all the government branches to contribute. The fire engulfing the criminal justice system was set by deception and must be extinguished to protect the American people before it is too late.

The Philosophical Stage. Drama and Dialectic in Classical Athens by Joshua Billings

Reviewed by: The Philosophical Stage. Drama and Dialectic in Classical Athens by Joshua Billings Anna Novokhatko Joshua Billings. The Philosophical Stage. Drama and Dialectic in Classical Athens. Princeton: Princeton University Press, 2021. Pp. xiv, 272. $39.95. ISBN 978-0-69-120518-2. Billings' study of the revival of the close and multifaceted relationship between philosophical and dramatic thought makes a challenging contribution to the fruitful discussions about the late Athenian polis, which witnessed a productive and competitive interaction of poetic and philosophical thought and writing. The book argues convincingly that Greek drama not only integrates developments in philosophical thought, but that dramatic texts are themselves developments in philosophical thought and should be recognised as part of the early Greek philosophical canon. The book consists of an introduction, three chapters, and a conclusion. The introduction explains in detail both general methodological issues and the main argument that Greek tragedy (and sometimes comedy) is most philosophical not when it reflects existing philosophical doctrines, but when it takes philosophical questions as its own. Chapter 1 considers the phenomenon of lists and catalogues in drama, particularly listing of inventions, gifts, achievements, or capacities that provides an image of human culture and reflects on the sources of authority and power in human existence. Palamedes and Prometheus were both associated with Aeschylean tragedy and allegorized the utopian image of the catalogue listing the capacities that raise humans above animals (frs. 181a, 182 and PV 447–506). Also, Theseus' speech of thanksgiving (Eur. Suppl. 163–249), Billings argues, represents the closest parallel to Prometheus' catalogue. The guiding idea of the speech is gratitude for the beneficial constituents of a teleologically ordered universe. The so-called Ode to Man from Sophocles' Antigone as well as the much-discussed Sisyphus Fragment (Euripides or Critias) are also brought into the philosophical context of major preoccupations of 5th century BCE intellectual culture on the central achievements of human culture and the role of the gods in human existence. Chapter 2 turns to another philosophical issue: the connection between deception and ontology, a significant question of the sophistic movement. Billings argues that dramas stage ontological questions as problems of ethical action and political community that concern language, truth, and being. Billings analyses the similarities of the four prologue scenes of Sophocles' Electra and Philoctetes, Aristophanes' Thesmophoriazusae, and Euripides' Iphigenia at Aulis. All four establish questions of reality, truth, and language that pervade the works as a whole and have a substantial presence in contemporary philosophical discourse. The "intrigue prologue" constitutes a form for probing the role of trust and truth within a community, where the problems of deception are raised in political terms, crystallizing questions of truth and knowledge within a democratic [End Page 93] collective. These texts constitute a reflection parallel to those of Parmenides or Gorgias on the relations among reality, perception, and persuasion. The plays problematize deception as a state between truth and falsehood, being and nonbeing, from which no one can fully emerge, least of all the audience. In forging a link between questions of reality and truth, on one hand, and political life, on the other, the "intrigue prologues" are argued to have made a distinctive contribution to fifth century philosophical thought. Chapter 3 deals with the questions of debate, intellectual authority, and novelty among three groups—politicians, poets, and craftsmen. The much-discussed idea of sophia closely linked to new forms of traditional socio-cultural authority as well as to novel intellectual trends is analysed in the agones sophias ("intellectual contests") in Euripides' Antiope and Palamedes, Aristophanes' Frogs, and Euripides' Bacchae. In Euripides' Antiope the two brothers Amphion and Zethus in debate contrast their active and contemplative lives while the agon represents a metadiscourse on musical and philosophical innovation in drama; in Euripides' Palamedes the confrontations over the meaning of sophia ("wisdom") are staged as well as questions surrounding the role of the poet in society and tragedy's role as a traditional source of wisdom. The agon in Aristophanes' Frogs puts into play various ideas of sophia as a conception connected to divinity and practice in a community and as human inquiry and individual pursuit. The play's thoroughgoing contrast between old and...