Formal Computational Model Research Articles

Is Gender Diversity a Threat or an Opportunity? Unpacking Firm Responses to a Societal Imperative

Papers in this symposium examine the responses to, and the consequences of, growing institutional and stakeholder demands for gender equality and inclusion in the labor market. While some firms substantively embrace the value of diversity, others do so only symbolically. Using formal theory, computational models, verbal reasoning, and empirical evidence, we aim to shed new light on the mechanisms and mediators that influence how firms respond to the societal imperative to embrace gender diversity. Intersectionality and Pay Equity Presenter: David Gaddis Ross; U. of Florida A Competitive-Conformity Explanation for Gender Diversity in Professional Service Firms Presenter: John Mawdsley; HEC Paris Presenter: Lionel Paolella; U. of Cambridge The Value Added of Women in the Board: Evidence from Two-Sided Matching Under Gender Quota Policies Presenter: Denisa Mindruta; HEC Paris Presenter: Elena Lizunova; HEC Paris Saris on Boards: The Consequences of Mandated Gender Quotas in Emerging Markets Presenter: Venkat Kuppuswamy; Northeastern U. Presenter: Rahul Anand; HEC Paris Gender Network Gap: When and How Do Women-Owned Firm Network Differently than Male-Owned Firms? Presenter: Shweta Gaonkar; Johns Hopkins U.

RoSCo: Robust Updates for Software-Defined Networks

In many Software-Defined Networking (SDN) deployments the control plane ends up being actually centralized, yielding a single point of failure and attack. This paper models the interaction between the data plane and a distributed control plane consisting of a set of failure-prone and potentially malicious (compromised) control devices, and implements a secure and robust controller platform that allows network administrators to integrate new network functionality as with a centralized approach. Concretely, the network administrator may program the data plane from the perspective of a centralized controller without worrying about distribution, asynchrony, failures, attacks, or coordination problems that any of these could cause. We introduce a formal SDN computation model for applying network policies and show that it is impossible to implement asynchronous non-blocking and strongly consistent SDN controller platforms in that model. We then present a ro bust S DN co ntroller protocol (RoSCo) which implements (i) a protocol with provably linearizable semantics for applying network policies that is resilient against faulty/malicious control devices as long as a correct majority exists, and (ii) a modification to the protocol that improves performance by relaxing the guarantees of linearizability to exploit commutativity among updates. Extensive experiments conducted with a functional prototype of RoSCo over a large networked infrastructure supporting Open vSwitch (OVS)-compatible Agilio CX™ SmartNIC hardware show that RoSCo induces bearable overhead. In fact, RoSCo achieves higher throughput in most cases investigated than the seminal Ravana platform which addresses only benign (crash) failures.

Theory of mind and decision science: Towards a typology of tasks and computational models

The ability to form a Theory of Mind (ToM), i.e., to theorize about others’ mental states to explain and predict behavior in relation to attributed intentional states, constitutes a hallmark of human cognition. These abilities are multi-faceted and include a variety of different cognitive sub-functions. Here, we focus on decision processes in social contexts and review a number of experimental and computational modeling approaches in this field. We provide an overview of experimental accounts and formal computational models with respect to two dimensions: interactivity and uncertainty. Thereby, we aim at capturing the nuances of ToM functions in the context of social decision processes. We suggest there to be an increase in ToM engagement and multiplexing as social cognitive decision-making tasks become more interactive and uncertain. We propose that representing others as intentional and goal directed agents who perform consequential actions is elicited only at the edges of these two dimensions. Further, we argue that computational models of valuation and beliefs follow these dimensions to best allow researchers to effectively model sophisticated ToM-processes. Finally, we relate this typology to neuroimaging findings in neurotypical (NT) humans, studies of persons with autism spectrum (AS), and studies of nonhuman primates.

Extending single- to multi-variant model transformations by trace-based propagation of variability annotations

Model-driven engineering involves the construction of models on different levels of abstraction. Software engineers are supported by model transformations, which automate the transition from high- to low-level models. Product line engineering denotes a systematic process that aims at developing different product variants from a set of reusable assets. When model-driven engineering is combined with product line engineering, engineers have to deal with multi-variant models. In annotative approaches to product line engineering, model elements are decorated with annotations, i.e., Boolean expressions that define the product variants in which model elements are to be included. In model-driven product line engineering, domain engineers require multi-variant transformations, which create multi-variant target models from multi-variant source models. We propose a reuse-based gray-box approach to realizing multi-variant model transformations. We assume that single-variant transformations already exist, which have been developed for model-driven engineering, without considering product lines. Furthermore, we assume that single-variant transformations create traces, which comprise the steps executed in order to derive target models from source models. Single-variant transformations are extended into multi-variant transformations by trace-based propagation: after executing a single-variant transformation, the resulting single-variant target model is enriched with annotations that are calculated with the help of the transformation’s trace. This approach may be applied to single-variant transformations written in different languages and requires only access to the trace, not to the respective transformation definition. We also provide a correctness criterion for trace-based propagation, and a proof that this criterion is satisfied under the prerequisites of a formal computational model.

Analysis and Identification of Possible Automation Approaches for Embedded Systems Design Flows

Sophisticated and high performance embedded systems are present in an increasing number of application domains. In this context, formal-based design methods have been studied to make the development process robust and scalable. Models of computation (MoC) allows the modeling of an application at a high abstraction level by using a formal base. This enables analysis before the application moves to the implementation phase. Different tools and frameworks supporting MoCs have been developed. Some of them can simulate the models and also verify their functionality and feasibility before the next design steps. In view of this, we present a novel method for analysis and identification of possible automation approaches applicable to embedded systems design flow supported by formal models of computation. A comprehensive case study shows the potential and applicability of our method.

A Service-Oriented Programming Approach for Dynamic Distributed Manufacturing Systems

Dynamic reconfigurability and adaptability are crucial features of the future manufacturing systems that must be supported by adequate software technologies. Currently, they are typically achieved as add-ons to existing software tools and run-time systems, which are not based on any formal foundation such as formal model of computation (MoC). This paper presents the new programming paradigm of service oriented SystemJ (SOSJ), which targets dynamic distributed software systems suited for future manufacturing applications. SOSJ is built on a merger and the synergies of two programming concepts of service oriented architecture, to support dynamic software system composition, and SystemJ programming language based on a formal MoC, which targets correct by construction design of static distributed software systems. The resulting programming paradigm allows the design and implementation of dynamic distributed software systems.

Towards a rational constructivist theory of cognitive development.

This article provides a synthesis and overview of a theory of cognitive development, rational constructivism. The basic tenets of this view are as follows: (a) Initial state: Human infants begin life with a set of proto-conceptual primitives. These early representations are not in the format of a language of thought. (b) Mature state: Human adults represent the world in terms of a set of domain-specific intuitive theories. (c) Three types of mechanisms account for learning, development, and conceptual change: language and symbol learning, Bayesian inductive learning, and constructive thinking. (d) The child is an active learner, and cognitive agency is part and parcel of development. I will discuss each of these tenets, and provide an overview of the kind of empirical evidence that supports this view. This is a non-Piagetian view though it is in the spirit of constructivist theories of development; this view emphasizes the utility of formal computational models in understanding learning and developmental change. Lastly, this view also has implications for the study of philosophy of mind and epistemology. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

What Underlies Political Polarization? A Manifesto for Computational Political Psychology

Polarization is one of the biggest societal challenges of our time, yet its drivers are poorly understood. Here we propose a novel approach - computational political psychology - which uses behavioral tasks in combination with formal computational models to identify candidate cognitive processes underpinning susceptibility to polarized beliefs about political and societal issues.

Influence, Interests and Information in Organizational Decision Making

We develop a formal computational model to analyze how diferenes in influence structures and interests shape organizational decision-making involving information aggregation. Our model reproduces s...

There's more to function than resting state neuroimaging: Cognition is key for developing stimulation therapeutics

Psychiatric neuro-stimulation research is entering an exciting period. Big Data neuroimaging has matured to the point of identifying distinct depression ‘biotypes’-reliable covariation of symptoms and resting-state circuit dysfunction - that explain variation in response to antidepressant neurostimulation [1] . This paves the way for future personalized treatment target selection, and testifies to stable brain network configurations underlying clinical phenotypes. How should we best stimulate such networks to durably improve symptoms? The majority of brain stimulation treatment protocols are designed physiologically. The aim is re-normalize network excitability, via targeted excitation/inhibition of dysfunctional network nodes. Through repeated stimulation over weeks, these artificially-induced transient changes in excitability become stabilized. The hope is that this will also change and re-normalize function. Yet information processing–the functions these networks compute–is not in fact measured, controlled or manipulated. The most reliable way to induce long-term adaptive behaviour (brain) change is to engage endogenous neurocognitive mechanisms of learning and memory. We propose that stimulation is likely to more efficiently normalize function when applied concurrent with an adaptive learning protocol, compared to stimulation in the resting state. We illustrate this with an example from our own work developing neurostimulation therapeutics for attention deficits post-stroke [2] . We aim to highlight the value of functionally targeted stimulation, and formal computational models in particular, for designing rational intervention protocols in which stimulation is used to change information processing in a quantifiable, adaptive direction, which consequently predicts behaviour change. Model parameters enabled us to interrogate individual differences that covaried - across information processing, white matter microstructure, functional connectivity and neurochemistry - and quantify their response to stimulation [3] . We propose that computational assays of behaviour are a potentially powerful tool with which to tackle the formiddable challenges of developing and translating cognitive neurostimulation protocols into next-generation treatments in psychiatry.

Plant behaviour in response to the environment: information processing in the solid state.

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. Research into biological processes with a computational perspective may yield new insights and provide a general framework for information processing across different substrates.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

Computational Models of Emotion Inference in Theory of Mind: A Review and Roadmap.

Research on social cognition has fruitfully applied computational modeling approaches to explain how observers understand and reason about others’ mental states. By contrast, there has been less work on modeling observers’ understanding of emotional states. We propose an intuitive theory framework to studying affective cognition—how humans reason about emotions—and derive a taxonomy of inferences within affective cognition. Using this taxonomy, we review formal computational modeling work on such inferences, including causal reasoning about how others react to events, reasoning about unseen causes of emotions, reasoning with multiple cues, as well as reasoning from emotions to other mental states. In addition, we provide a roadmap for future research by charting out inferences—such as hypothetical and counterfactual reasoning about emotions—that are ripe for future computational modeling work. This framework proposes unifying these various types of reasoning as Bayesian inference within a common “intuitive Theory of Emotion.” Finally, we end with a discussion of important theoretical and methodological challenges that lie ahead in modeling affective cognition.

Dynamic Reconfiguration and Adaptation of Manufacturing Systems Using SOSJ Framework

One of the key challenges in modern manufacturing systems is how to dynamically reconfigure software behaviors that govern machines to reflect changes in physical manufacturing process without completely resetting the entire manufacturing operation. The existing software solutions used to describe software behaviors in manufacturing systems are typically not based on formal semantics and model of computation and have limited capabilities in handling dynamic adaptation/reconfiguration. This paper presents the Service-Oriented SystemJ (SOSJ) framework that supports a new programming paradigm for designing dynamic distributed manufacturing systems. SOSJ combines the system-level language SystemJ and service-oriented architecture (SOA) paradigm to take advantages of both SystemJ's correct-by-construction formal semantics and SOA's dynamic features, respectively. The paper describes the concepts and functionalities of SOSJ, which enable dynamic reconfiguration of a typical manufacturing system. Performance benchmarks are run to compare the capabilities of SOSJ to a multiagent system framework JADE.

A Systematic and Realistic Network-on-Chip Traffic Modeling and Generation Technique for Emerging Many-Core Systems

As programs for microprocessor architectures, network-on-chip (NoC) traffic patterns are essential tools for NoC performance assessment and design exploration. The fidelity of NoC traffic patterns has profound influence on NoC studies. In this paper, we present a systematic traffic modeling and generation methodology and a traffic suite for efficient evaluation of NoC-based many-core systems. The publicly released MCSL (multi-constraint system-level) traffic suite includes a set of realistic traffic patterns for real-world applications and covers popular NoC architectures. It captures both the communication behaviors in NoCs and the temporal dependencies among them. The MCSL traffic suite can be easily incorporated into existing NoC simulators and significantly improve NoC simulation accuracy. The proposed methodology uses formal computational models to capture both communication and computation requirements of applications. It optimizes application memory requirements, mapping, and scheduling to maximize overall system performance and utilization before extracting traffic patterns through cycle level simulations. Experiment results show that the MCSL traffic suite can be used to study NoC characteristics more accurately than traditional random traffic patterns.

Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement

Visual attention is a kind of fundamental cognitive capability that allows human beings to focus on the region of interests (ROIs) under complex natural environments. What kind of ROIs that we pay attention to mainly depends on two distinct types of attentional mechanisms. The bottom-up mechanism can guide our detection of the salient objects and regions by externally driven factors, i.e. color and location, whilst the top-down mechanism controls our biasing attention based on prior knowledge and cognitive strategies being provided by visual cortex. However, how to practically use and fuse both attentional mechanisms for salient object detection has not been sufficiently explored. To the end, we propose in this paper an integrated framework consisting of bottom-up and top-down attention mechanisms that enable attention to be computed at the level of salient objects and/or regions. Within our framework, the model of a bottom-up mechanism is guided by the gestalt-laws of perception. We interpreted gestalt-laws of homogeneity, similarity, proximity and figure and ground in link with color, spatial contrast at the level of regions and objects to produce feature contrast map. The model of top-down mechanism aims to use a formal computational model to describe the background connectivity of the attention and produce the priority map. Integrating both mechanisms and applying to salient object detection, our results have demonstrated that the proposed method consistently outperforms a number of existing unsupervised approaches on five challenging and complicated datasets in terms of higher precision and recall rates, AP (average precision) and AUC (area under curve) values.

Reproducible Evaluation of System Efficiency With a Model of Architecture: From Theory to Practice

Current trends in high performance and embedded computing include design of increasingly complex hardware architectures with high parallelism, heterogeneous processing elements, and nonuniform communication resources. In order to take hardware and software design decisions, early evaluations of the system nonfunctional properties are needed. These evaluations of system efficiency require electronic system-level information on both algorithms and architecture. Contrary to algorithm models for which a major body of work has been conducted on defining formal models of computation (MoCs), architecture models from the literature are mostly empirical models from which reproducible experimentation requires the accompanying software. In this paper, a precise definition of a model of architecture (MoA) is proposed that focuses on reproducibility and abstraction and removes the overlap previously existing between the notions of MoA and MoC. A first MoA, called the linear system-level architecture model (LSLA), is presented. To demonstrate the generic nature of the proposed new architecture modeling concepts, we show that the LSLA model can be integrated flexibly with different MoCs. LSLA is then used to model the energy consumption of a state-of-the-art multiprocessor system-on-chip (MPSoC) when running an application described using the synchronous dataflow MoC. A method to automatically learn LSLA model parameters from platform measurements is introduced. Despite the high complexity of the underlying hardware and software, a simple LSLA model is demonstrated to estimate the energy consumption of the MPSoC with a fidelity of 86%.

An AmI-based and privacy-preserving shopping mall model

Nowadays, large shopping malls provide tools to help and boost customers to buy products. Some of these tools melt down digital operations with physical ones executed by customers into blended commerce experiences. On the other hand, ambient intelligence (AmI) represents a paradigm focused on equipping physical environments to define ergonomic spaces for people interacting with computer-based localized services which are ubiquitously accessible. In this context, we propose a framework based on cellular automata (CA), a very well known formal computational model, suitable to abstract services deployed into an AmI-based environment preserving certain privacy levels of shoppers’ information. CA-based algorithms are advantageous because they are distributed, scalable, on-line and require low costs to be deployed. This work proposes a recent application of CA, namely Cellular ANTomata, to implement a service by which shoppers are guided to find the suitable offerings for items in their shopping lists. A further result provided by this paper is the instantiation of a protocol for privacy-preserving shopping experience in the shopping mall.

The Effort Paradox: Effort is Both Costly and Valued

According to prominent models in cognitive psychology, neuroscience, and economics, effort—be it physical or mental—is costly: when given a choice, humans and non-human animals alike tend to avoid effort. Here, we suggest that the opposite is also true and review extensive evidence that effort can also add value. Not only can the same outcomes be more rewarding if we apply more (not less) effort, sometimes we select options precisely because they require effort. Given the increasing recognition of effort’s role in motivation, cognitive control, and value-based decision-making, considering this neglected side of effort will not only improve formal computational models, but also provide clues about how to promote sustained mental effort across time.

Formal framework for distributed swarm computing: abstract model and properties

Swarm computing is an emerging computing paradigm suitable for solving difficult optimization problems by employing a nature-inspired search for solutions. A set of entities called swarm entities populates a digital space that emulates a real physical environment. The digital space acts as a container of swarm entities by providing the basic functionalities for the entities management including processing and migration. Swarm entities are modeled as computational objects that follow a specific set of behavioral laws of natural inspiration. The entities are organized as a swarm, i.e., they coordinate, collaborate and act by exchanging information either directly or indirectly via the environment, seeking to solve a difficult computational optimization problem. Our main research result reported in this paper is the proposal of a new formal computational model of a generic distributed framework for swarm computing. Our model captures the basic computational properties of the swarm using the formal language of Finite State Process algebra. The proposed model is simple, clear and abstract, i.e., technology independent. It can serve as a starting basis for further refinement and subsequent development of new concurrent and/or distributed implementations using the available distributed computing technologies.

The dynamics of team cognition: A process-oriented theory of knowledge emergence in teams.

Team cognition has been identified as a critical component of team performance and decision-making. However, theory and research in this domain continues to remain largely static; articulation and examination of the dynamic processes through which collectively held knowledge emerges from the individual- to the team-level is lacking. To address this gap, we advance and systematically evaluate a process-oriented theory of team knowledge emergence. First, we summarize the core concepts and dynamic mechanisms that underlie team knowledge-building and represent our theory of team knowledge emergence (Step 1). We then translate this narrative theory into a formal computational model that provides an explicit specification of how these core concepts and mechanisms interact to produce emergent team knowledge (Step 2). The computational model is next instantiated into an agent-based simulation to explore how the key generative process mechanisms described in our theory contribute to improved knowledge emergence in teams (Step 3). Results from the simulations demonstrate that agent teams generate collectively shared knowledge more effectively when members are capable of processing information more efficiently and when teams follow communication strategies that promote equal rates of information sharing across members. Lastly, we conduct an empirical experiment with real teams participating in a collective knowledge-building task to verify that promoting these processes in human teams also leads to improved team knowledge emergence (Step 4). Discussion focuses on implications of the theory for examining team cognition processes and dynamics as well as directions for future research. (PsycINFO Database Record