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Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert to their original value as the system converges to equilibrium. With the help of the causal ordering algorithm, one can construct graphical representations of dynamical systems that represent the causal relations between the variables and the conditional independences in the equilibrium distribution. We apply these tools to formulate sufficient graphical conditions for identifying perfect adaptation from a set of first-order differential equations. Furthermore, we give sufficient conditions to test for the presence of perfect adaptation in experimental equilibrium data. We apply this method to a simple model for a protein signalling pathway and test its predictions in both simulations and using real-world protein expression data. We demonstrate that perfect adaptation can lead to misleading orientation of edges in the output of causal discovery algorithms.

Complex social scientific theories are conventionally tested using linear structural equation modeling (SEM). However, the underlying assumptions of linear SEM often prove unrealistic, making the decomposition of direct and indirect effects problematic. Recent advancements in causal mediation analysis can help to address these shortcomings, allowing for causal inference when a new set of identifying assumptions are satisfied. This article reviews how these ideas can be generalized to multiple mediators, with a focus on the posttreatment confounding and causal ordering cases. Using the potential outcome framework as a rigorous tool for causal inference, the application is the theory of procedural justice policing. Analysis of data from two randomized experiments shows that making similar parametric assumptions to SEMs and using G-computation improve the viability of effect decomposition. The article concludes with a discussion of how causal mediation analysis improves upon SEM and the potential limitation of the methods. [ABSTRACT FROM AUTHOR]

Emerging applications based on vehicular ad-hoc networks (VANETs) are focused on offering new services to intelligent transportation systems (ITSs), such as traffic safety and management. For such kind of services, there are intrinsic spatial and temporal dependencies in the communication among mobile and fixed components. However, the characteristics of VANETs, which include a rapidly changing topology and the lack of global temporal references, make it difficult to satisfy such communication constraints. Commonly, the communication protocols for VANETs assume high coupled communication links; nevertheless, for most ITSs, the entities to whom such information will be useful are determined at run time by the spatial and temporal context. In this paper, a causal position-based indirect communication protocol is proposed, which allows road side units (RSUs) to disseminate messages with an uncoupled communication. The proposed solution uses the vehicles as opportunistic carriers, leveraging the bounded movement in a specific geographical region to exchange data among RSUs even with unknown recipients. To preserve the coherence of the exchange of messages, they are ordered by establishing their causal dependencies without the requirement of global references or synchronized physical clocks. Furthermore, the proposed protocol leverages message redundancy to recover some lost messages during the transmission. It is analytically demonstrated that the protocol is scalable since the size of the control information depends only on the number of RSUs rather than on the total number of entities in the system.

We describe a performance budget planning model developed for a research university, comprised of a set of 88 key variables and 38 non-linear structural equations that describe interactions among them. These equations, based on the knowledge of research university's financial working and theoretical considerations, relate expenditures and revenues to teaching and research operations. We demonstrate the value of this model for developing insight into the financial structure of the university. In particular, we show how the model aids in (1) comparing the effect of various policy alternatives on the performance of the university, (2) performing comparative statics analysis of any subset of variables of interest, (3) choice of policy variables and policy alternatives, and (4) gaining insight into the structure of the interactions for a given policy alternative in terms of the causal chain between policy variables and outcome variables. We also describe a computer implementation of the model and discuss a class of mathematical tools for policy planning analysis that facilitate the use and manipulation of models based on sets of nonlinear constraints. [ABSTRACT FROM AUTHOR]

Real-world complex systems are often modelled by sets of equations with endogenous and exogenous variables. What can we say about the causal and probabilistic aspects of variables that appear in these equations without explicitly solving the equations? We make use of Simon's causal ordering algorithm (Simon, 1953) to construct a causal ordering graph and prove that it expresses the effects of soft and perfect interventions on the equations under certain unique solvability assumptions. We further construct a Markov ordering graph and prove that it encodes conditional independences in the distribution implied by the equations with independent random exogenous variables, under a similar unique solvability assumption. We discuss how this approach reveals and addresses some of the limitations of existing causal modelling frameworks, such as causal Bayesian networks and structural causal models. [ABSTRACT FROM AUTHOR]

Mobile distributed systems (MDS) deal with data transmissions among geographically distributed mobile sources. In this environment, the preservation of temporal dependencies among exchanged real-time multimedia data is mainly affected the asynchronous transmissions, by constant topology changes, unpredictable delays, as well as the lack of global memory and clock. Existing solutions for distributed intermedia synchronization are based on the assumption of a homogeneous end-to-end system, where symmetrical unbounded overhead cannot affect the throughput. This assumption, however, is not feasible for MDS due to the constraints of the wireless medium and the mobility implications. This paper presents a distributed multimedia synchronization protocol oriented to satisfy logical and temporal dependencies in the exchange of real-time data in MDS by using logical mapping, avoiding the use of global references. Two main aspects of the protocol include the computation of the deadline for messages by using only relative time points and by dividing the processing stage to achieve synchronization with an asymmetric principle of design. With the simulations results we demonstrate that our protocol is effective in diminishing the synchronization error. Furthermore, the protocol is efficient as regards processing and storage costs at the mobile hosts, and in the overhead attached per message with a reduced usage of bandwidth across the wired and wireless channels in comparison with the Real-time Transport Protocol.

Peer-to-peer applications such as multiplayer online games are characterized by considering group communication among geographically distributed peers. In such environments, causal ordering is an essential property for consistent exchange of information among peers. Although several works are oriented to ensure message causal order, most of them are not suitable for hierarchical overlay networks. In this paper, we propose an efficient causal protocol oriented to be used in a hierarchical overlay network. In our protocol the overhead timestamp per message is based on the number of messages with immediate dependency relation. By using the information about network architecture and representing message dependencies on a bit level, proposed protocol ensures causal message ordering without enforcing super peers order to all of the peers in a group. The protocol has been simulated and the results show that it presents lower overhead than currently existing causal protocols. [ABSTRACT FROM AUTHOR]

In this note we provide a concise report on the complexity of the causal ordering problem, originally introduced by Simon to reason about causal dependencies implicit in systems of mathematical equations. We show that Simon's classical algorithm to infer causal ordering is NP-Hard—an intractability previously guessed but never proven. We present then a detailed account based on Nayak's suggested algorithmic solution (the best available), which is dominated by computing transitive closure—bounded in time by O ( | V | ⋅ | S | ) , where S ( E , V ) is the input system structure composed of a set E of equations over a set V of variables with number of variable appearances (density) | S | . We also comment on the potential of causal ordering for emerging applications in large-scale hypothesis management and analytics. [ABSTRACT FROM AUTHOR]

In peer-to-peer (P2P) overlay networks, a group of n (2) peer processes have to cooperate with each other. Each peer sends messages to every peer and receives messages from every peer in a group. In group communications, each message sent by a peer is required to be causally delivered to every peer. Most of the protocols designed to ensure causal message order are designed for networks with a plain architecture. These protocols can be adapted to use in free scale and hierarchical topologies; however, the amount of control information is O(n), where n is the number of peers in the system. Some protocols are designed for a free scale or hierarchical networks, but in general they force the whole system to accomplish the same order viewed by a super peer. In this paper, we present a protocol that is specifically designed to work with a free scale peer-to-peer network. By using the information about the network's architecture and by representing message dependencies on a bit level, the proposed protocol ensures causal message ordering without enforcing super peers order. The designed protocol is simulated and compared with the Immediate Dependency Relation and the Dependency Sequences protocols to show its lower overhead. [ABSTRACT FROM AUTHOR]

Background Goal theory research has mostly focused on the unidirectional effects of goals on learning strategies and academic achievement. Reciprocal relationships have mostly been neglected. Aims The primary aim of this study was to examine the reciprocal relations and causal ordering of mastery goals, metacognitive strategy use, and academic achievement. Sample A total of 8,773 secondary students drawn from three cohorts in Hong Kong participated in the study. Results We found that mastery goals and metacognitive strategy use exhibited reciprocal effects. Interestingly, and contrary to previous research, academic achievement was shown to predict mastery goal adoption and metacognitive strategy use but not the other way around. Results of the model were shown to be invariant across students of different genders, year levels, and school bands. Implications for goal theory research are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the QoS guaranteed causal ordering group communication for cognitive radio (CR) ad hoc networks. The issue of causal ordering has been studied extensively by previous works. However, these works considered the problem at the transport layer and the methods in the works incurred high communication overhead and the long latency for message delivery. In this paper, we discuss the causal ordering at the network layer. The problem of our concern is: given a QoS group communication request in a CR ad hoc network, how to set up the connection so that the causal ordering of the group communication can be preserved, and the bandwidth consumption of the communication is minimized under the condition that the QoS requirement is satisfied. In this paper, we focus on the bandwidth requirement. We propose a two-phase method to solve the problem. In the method, we first construct a multicast tree for the communication, and then assign slots for all tree links. It is proved that the method can preserve the causal ordering of messages without extra communication overhead nor the latency for delivering messages. Simulations are conducted to show the performance of our proposed method. [ABSTRACT FROM AUTHOR]

In peer-to-peer (P2P) overlay networks, a group of n (≥2) peer processes have to cooperate with each other. Each peer sends messages to every peer and receives messages from every peer in a group. In group communications, each message sent by a peer is required to be causally delivered to every peer. Most of the protocols designed to ensure causal message order are designed for networks with a plain architecture. These protocols can be adapted to use in free scale and hierarchical topologies; however, the amount of control information is O(n), where n is the number of peers in the system. Some protocols are designed for a free scale or hierarchical networks, but in general they force the whole system to accomplish the same order viewed by a super peer. In this paper, we present a protocol that is specifically designed to work with a free scale peer-to-peer network. By using the information about the network’s architecture and by representing message dependencies on a bit level, the proposed protocol ensures causal message ordering without enforcing super peers order. The designed protocol is simulated and compared with the Immediate Dependency Relation and the Dependency Sequences protocols to show its lower overhead.

In this paper, we present an efficient causal algorithm that can be used in multicast communication environments, in particular the overlapping multi-channel case, where a participant can belong to and communicate through more than one multicast channel. The causal algorithm is built on the paradigm of group communication. The groups are established according to the participant channel subscription. In order to reduce the amount of control information (CI), we propose an extension of the immediate dependency relation (IDR), which was introduced by Peterson in the context of one group. This IDR extension allows us to define necessary and sufficient CI to ensure causal delivery in a multi-group environment. We show that through the use of the IDR extension, we reduce the amount of CI sent per message without imposing restrictions on interaction or execution (e.g. network topology, rediffusion servers, execution models, etc.). These characteristics allow our algorithm to be suitable for use in large distributed decentralised systems. We show the efficiency of our causal algorithm in terms of the overhead timestamped per message. [ABSTRACT FROM AUTHOR]

Previous research has suggested that in Britain older, less educated and working class voters tend to be less supportive of Europe than the young, educated and middle class. This research examines the sociological and psychological influences on opinions towards the European Union (EU) over time in Britain. It builds on existing insights from both single-country and cross-national studies of the sources of support for the EU and examines the main theoretical approaches based on interests, identities and domestic politics. It uses six BES cross-section surveys, binomial logistic regression and a multi-stage causal model for the period 1983–2005 to assess the apparent impact of a range of variables on public support for Europe. This enables estimation of the total and indirect effect for each explanatory variable and provides a more detailed and comprehensive review of the causes of opinion on the European issue. As the analysis covers a period of over two decades, it is also able to gauge whether the factors underpinning support or opposition for the EU have changed over time.British Politics (2009) 4, 47–82. doi:10.1057/bp.2008.39 [ABSTRACT FROM AUTHOR]

The preservation of temporal dependencies among different media data, such as text, still images, video and audio, and which have simultaneous distributed sources as origin, is an open research area and an important issue for emerging distributed multimedia systems, such as Teleimmersion, Telemedicine, and IPTV. Although there are several works oriented to satisfy temporal dependencies in distributed multimedia systems, they are far from resolving the problem. In this paper we propose a logical synchronization model able to specify at runtime any kind of temporal relationship among the distributed multimedia data involved in a temporal scenario. The synchronization model is based on a new concept that we call logical mapping. A logical mapping, in general terms, translates a temporal relation based on a timeline to be expressed according to its causal dependencies. The logical mappings allow us to avoid the use of global references, such as a wall clock and shared memory. We note that the proposed intermedia synchronization model does not require previous knowledge of when, nor of how long, the media involved of a temporal scenario is executed. Finally, in order to show the viability of the proposed model, a syncrhonization approach is presented. [ABSTRACT FROM AUTHOR]

An optimal causal message ordering algorithm for asynchronous distributed systems was proposed by Kshemkalyani and Singhal and its optimalily was proven theoretically. For a system of n processes, although the space complexity of this algorithm was shown to be O(n[sup2]) integers, it was expected that the actual space overhead would be much less than n[sup2]. It is difficult to determine the behavior of this algorithm by a theoretical analysis. In this paper, we measure the overheads of two different implementations of the optimal causal message ordering algorithm via simulation under a wide range of system conditions. The optimal algorithm is seen to display significantly less message space overhead and log space overhead than the canonical Raynal-Schiper-Toueg algorithm. [ABSTRACT FROM AUTHOR]

The paper shows that characterizing the causal relationship between significant events is an important but non-trivial aspect for understanding the behavior of distributed programs. An introduction to the notion of causality and its relation to logical time is given; some fundamental results concerning the characterization of causality are presented. Recent work on the detection of causal relationships in distributed computations is surveyed. The issue of observing distributed computations in a causally consistent way and the basic problems of detecting global predicates are discussed. To illustrate the major difficulties, some typical monitoring and debugging approaches are assessed, and it is demonstrated how their feasibility is severely limited by the fundamental problem to master the complexity of causal relationships. [ABSTRACT FROM AUTHOR]

• Longitudinal study assessed stability of road safety deterrence-related perceptions. • Stability of both legal and non-legal perceptions was assessed over 3 months. • Fluctuations were found in all deterrence-related perceptions. • Certainty of apprehension had the largest fluctuation. • Implications for methodology used in road safety deterrence studies. This study addressed the causal ordering problem in deterrence research by examining the perceptual stability of deterrence variables over time and comparing the results via cross-sectional and longitudinal surveys. This research extends upon scant previous research by including three key classical deterrence variables (i.e., the perceived certainty of apprehension and the perceived severity and swiftness of punishment), as well as Homel's (1988) extra-legal deterrence-related variables of the fear of physical loss, material loss and social sanctions. A longitudinal survey design was utilised over a three month-time period (N = 200, M age = 20.38 years, 71 males) that examined the stability of the deterrence-related variables for three road rule violations, consisting of 1) exceeding the speed limit by more than 10 km/hr, 2) reading a message on a phone while driving and 3) using the social media platform of Snapchat while driving. Overall, fluctuations were found in all the deterrence-related variables (both legal and extra-legal sanctions), with the largest difference being for the perceptions of the certainty of apprehension. Consequently, it can be suggested that: (a) longitudinal surveys are more reliable when measuring the impact of deterrence perceptions on engagement in offending behaviour and (b) the problem of causal ordering regarding utilising cross-sectional surveys is further illuminated. The results suggest that deterrence is a dynamic process, constantly changing based upon individual experiences, which reinforces the need for continued enforcement efforts (both legal and non-legal), within the road safety arena to maximise rule compliance. [ABSTRACT FROM AUTHOR]

We describe a performance budget planning model developed for a research university, comprised of a set of 88 key variables and 38 non-linear structural equations that describe interactions among them. These equations, based on the knowledge of research university's financial working and theoretical considerations, relate expenditures and revenues to teaching and research operations. We demonstrate the value of this model for developing insight into the financial structure of the university. In particular, we show how the model aids in (1) comparing the effect of various policy alternatives on the performance of the university, (2) performing comparative statics analysis of any subset of variables of interest, (3) choice of policy variables and policy alternatives, and (4) gaining insight into the structure of the interactions for a given policy alternative in terms of the causal chain between policy variables and outcome variables. We also describe a computer implementation of the model and discuss a class of mathematical tools for policy planning analysis that facilitate the use and manipulation of models based on sets of nonlinear constraints.