General Process Theory Research Articles

Direct social perception and dual process theories of mindreading.

The direct social perception (DSP) thesis claims that we can directly perceive some mental states of other people. The direct perception of mental states has been formulated phenomenologically and psychologically, and typically restricted to the mental state types of intentions and emotions. I will compare DSP to another account of mindreading: dual process accounts that posit a fast, automatic "Type 1" form of mindreading and a slow, effortful "Type 2" form. I will here analyze whether dual process accounts' Type 1 mindreading serves as a rival to DSP or whether some Type 1 mindreading can be perceptual. I will focus on Apperly and Butterfill's dual process account of mindreading epistemic states such as perception, knowledge, and belief. This account posits a minimal form of Type 1 mindreading of belief-like states called registrations. I will argue that general dual process theories fit well with a modular view of perception that is considered a kind of Type 1 process. I will show that this modular view of perception challenges and has significant advantages over DSP's phenomenological and psychological theses. Finally, I will argue that if such a modular view of perception is accepted, there is significant reason for thinking Type 1 mindreading of belief-like states is perceptual in nature. This would mean extending the scope of DSP to at least one type of epistemic state.

Andô–Douglas type characterization of optional projections and predictable projections

Optional projections and predictable projections of stochastic processes play important roles in the general theory of stochastic processes, semimartingale theory and stochastic calculus. They share some important properties with ordinary conditional expectations and generalized conditional expectations. While the characterization of ordinary conditional expectations and generalized conditional expectations has been studied by several authors, no similar work has been done for optional projections and predictable projections. This paper aims at undertaking this task by giving Andô–Douglas type characterization theorem for both.

Dynamic Programming for General Linear Quadratic Optimal Stochastic Control with Random Coefficients

We are concerned with the linear-quadratic optimal stochastic control problem where all the coefficients of the control system and the running weighting matrices in the cost functional are allowed to be predictable (but essentially bounded) processes and the terminal state-weighting matrix in the cost functional is allowed to be random. Under suitable conditions, we prove that the value field $\mathbb{V}(t,x,\omega), (t,x,\omega)\in [0,T]\times R^n\times \Omega$, is quadratic in $x$ and has the following form: $\mathbb{V}(t,x)=\langle \mathbb{K}_tx, x\rangle$, where $\mathbb{K}$ is an essentially bounded nonnegative symmetric matrix-valued adapted processes. Using the dynamic programming principle (DPP), we prove that $\mathbb{K}$ is a continuous semimartingale of the form $\mathbb{K}_t=\mathbb{K}_0-\int_0^t \, dk_s+\int_0^t\sum_{i=1}^d L_s^i\, dW_s^i, \ t\in [0,T]$, with $k$ being a continuous process of bounded variation and the stochastic integral $\int_0^\cdot\sum_{i=1}^d L^i_s\, dW^i_s$ being a bounded mean oscillation martingale, and that $(\mathbb{K}, L)$ with $L:=(L^1, \ldots, L^d)$ is a solution to the associated backward stochastic Riccati equation (BSRE), whose generator is highly nonlinear in the unknown pair of processes. The uniqueness is also proved via a localized completion of squares in a self-contained manner for a general BSRE. The existence and uniqueness of an adapted solution to a general BSRE was initially proposed by the French mathematician J. M. Bismut [SIAM J. Control Optim., 14 (1976), pp. 419--444; Lecture Notes in Math. 649, C. Dellacherie, P. A. Meyer, and M. Weil, eds., Springer-Verlag, Berlin, 1978, pp. 180--264] and subsequently listed by S. Peng [Control of Distributed Parameter and Stochastic Systems (Hangzhou, 1998), S. Chen et al., eds., Kluwer, Boston, 1999, pp. 265--273] as an open problem for backward stochastic differential equations. It had remained open until a general solution by the author [SIAM J. Control Optim., 42 (2003), pp. 53--75] via the stochastic maximum principle with a viewpoint of stochastic flow for the associated stochastic Hamiltonian system. The present paper is its companion and gives the second but more comprehensive (seemingly much simpler, but appealing to the advanced tool of Doob--Meyer's decomposition theorem and the general theory of stochastic processes, in addition to the DPP) adapted solution to a general BSRE via the DPP. Further extensions to the jump-diffusion control system and to the general nonlinear control system are possible.

From Biological Constraints to Flexible Behavior Systems:Extending Our Knowledge of Sexual Conditioning in Japanese Quail

The traditional learning view involves the general process theory of learning that focuses on identifying universal principles that apply to all species capable of learning from experience, and that operate across a wide variety of situations. Examples of behavior that contradict general-process conceptions of learning have been in the past referred to as “biological constraints”. Traditional learning theorists choose to consider these examples as exceptions to otherwise universal principles of learning. On the contrary, the typical ethologist is more likely to be concerned with how specific behaviors may have evolved and in an animal’s species typical responses to stimuli they are likely to encounter in their natural environment. However, they also fail to embrace animal learning phenomena that occurs in the laboratory into their theoretical framework. Behavior systems represent an alternative to this view by providing a link between traditional views of learning and ethology. They conceptualize experiential learning not as a set of universal principles, butas species typical processes that reflect the specific demands of the ecological niche in which the species evolved. The current paper reviews and brings-to-date Domjan’s formulation of a sexual behavior system in male Japanese quail. The system includes a stimulus dimension consisting of species typical cues, local cues, and contextual cues, and a response dimension consisting of general search, focal search, and copulatory behavior. Domjan’sformulation includes two diagrams that include symbols that represent unconditioned and conditioned effects within the system. Our modification of the system focuses on additional and up-to-date conditioned effects. In general, adding conditioning to the system increases potential stimulus and response variation, thereby increasing the flexibility of the system as it has evolved as a result of continued observation and experimentation.

The Dynamic Process of Being (a Person): Two Process-Ontological Theories of Personal Identity

Abstract The purpose of this article is to introduce, interpret, and develop two incompatible process-ontological theories of personal identity that have received little attention in analytic metaphysics. The first theory derives from the notion of personal identity proposed in Alfred North Whitehead’s philosophy, but I interpret this notion differently from previous commentators. The Whiteheadian theory may appeal to those who believe that personal identity involves an entity or entities that are essentially dynamic, but has nothing to do with diachronic objectual identity: the (putative) binary equivalence relation that every entity bears to itself, and only to itself even after undergoing intrinsic change. The Whiteheadian theory may also find favor with those who, like Whitehead, reject the possibility of pure processes and hold that in every becoming, something—which need not be an object, thing, or individual substance—becomes. The second theory derives from the notion of recurrent dynamics presented in Johanna Seibt’s General Process Theory. The Seibtian theory may appeal to those who believe that personal identity involves not only an entity or entities that are essentially dynamic, but also the relation of diachronic objectual identity. The Seibtian theory may also find favor with those who, like Broad and Sellars, find reason to postulate pure processes.

Geometry of iteration stable tessellations: Connection with Poisson hyperplanes

Since the seminal work by Nagel and Weiss, the iteration stable (STIT) tessellations have attracted considerable interest in stochastic geometry as a natural and flexible, yet analytically tractable model for hierarchical spatial cell-splitting and crack-formation processes. We provide in this paper a fundamental link between typical characteristics of STIT tessellations and those of suitable mixtures of Poisson hyperplane tessellations using martingale techniques and general theory of piecewise deterministic Markov processes (PDMPs). As applications, new mean values and new distributional results for the STIT model are obtained.

Encapsulation of Particle Ensembles in Graphene Nanosacks as a New Route to Multifunctional Materials

Hybrid nanoparticles with multiple functions are of great interest in biomedical diagnostics, therapies, and theranostics but typically require complex multistep chemical synthesis. Here we demonstrate a general physical method to create multifunctional hybrid materials through aerosol-phase graphene encapsulation of ensembles of simple unifunctional nanoparticles. We first develop a general theory of the aerosol encapsulation process based on colloidal interactions within drying microdroplets. We demonstrate that a wide range of cargo particle types can be encapsulated, and that high pH is a favorable operating regime that promotes colloidal stability and limits nanoparticle dissolution. The cargo-filled graphene nanosacks are then shown to be open structures that rapidly release soluble salt cargoes when reintroduced into water, but can be partially sealed by addition of a polymeric filler to achieve slow release profiles of interest in controlled release or theranostic applications. Finally, we demonstrate an example of multifunctional material by fabricating graphene/Au/Fe3O4 hybrids that are magnetically responsive and show excellent contrast enhancement as multimodal bioimaging probes in both magnetic resonance imaging and X-ray computed tomography in full-scale clinical instruments.

Sesquilinear quantum stochastic analysis in Banach space

A theory of quantum stochastic processes in Banach space is initiated. The processes considered here consist of Banach space valued sesquilinear maps. We establish an existence and uniqueness theorem for quantum stochastic differential equations in Banach modules, show that solutions in unital Banach algebras yield stochastic cocycles, give sufficient conditions for a stochastic cocycle to satisfy such an equation, and prove a stochastic Lie–Trotter product formula. The theory is used to extend, unify and refine standard quantum stochastic analysis through different choices of Banach space, of which there are three paradigm classes: spaces of bounded Hilbert space operators, operator mapping spaces and duals of operator space coalgebras. Our results provide the basis for a general theory of quantum stochastic processes in operator spaces, of which Lévy processes on compact quantum groups is a special case.

Continuous-Time Public Good Contribution Under Uncertainty

We study a continuous-time problem of optimal public good contribution under uncertainty for an economy with an finite number of agents. Each agent can allocate his wealth between private consumption and repeated but irreversible contributions to increase the stock of some public good. We study the corresponding social planner problem and the case of strategic interaction between the agents and we characterize the optimal investment policies by a set of necessary and sufficient stochastic Kuhn-Tucker conditions. Suitably combining arguments from Duality Theory and the General Theory of Stochastic Processes, we prove an abstract existence result for a Nash equilibrium of our public good contribution game. Also, we show that our model exhibits a dynamic free rider effect. We explicitly evaluate it in a symmetric Black-Scholes setting with Cobb-Douglas utilities and we show that uncertainty and irreversibility of public good provisions do not affect free-riding.

Steady-state parameter sensitivity in stochastic modeling via trajectory reweighting

Parameter sensitivity analysis is a powerful tool in the building and analysis of biochemical network models. For stochastic simulations, parameter sensitivity analysis can be computationally expensive, requiring multiple simulations for perturbed values of the parameters. Here, we use trajectory reweighting to derive a method for computing sensitivity coefficients in stochastic simulations without explicitly perturbing the parameter values, avoiding the need for repeated simulations. The method allows the simultaneous computation of multiple sensitivity coefficients. Our approach recovers results originally obtained by application of the Girsanov measure transform in the general theory of stochastic processes [A. Plyasunov and A. P. Arkin, J. Comput. Phys. 221, 724 (2007)]. We build on these results to show how the method can be used to compute steady-state sensitivity coefficients from a single simulation run, and we present various efficiency improvements. For models of biochemical signaling networks, the method has a particularly simple implementation. We demonstrate its application to a signaling network showing stochastic focussing and to a bistable genetic switch, and present exact results for models with linear propensity functions.

An axiomatic approach to the valuation of cash flows

We model a stream of cash flows as an optional stochastic process, and value the cash flows by using a continuous and strictly positive linear functional. By applying a representation theorem from the general theory of stochastic processes we are able to study this valuation principle, as well as properties of the stochastic discount factor it implies. This approach to valuation is useful in the non-presence of a financial market, as is often the case when valuing cash flows arising from insurance contracts and in the application of real options.

An analysis of seismic scattering attenuation in a random elastic medium

Seismic attenuation has been an interesting topic of research, for it reflects the inherent media characteristics in which seismic waves propagate. There are many factors that cause seismic wave attenuation, such as geometry attenuation caused by energy dissipating during propagation, friction attenuation by relative sliding among rock grains, and scattering attenuation by rock heterogeneity. In this paper we study P-wave scattering attenuation in a random elastic medium by numerical simulations from a statistical point of view. A random elastic medium model is built based on general stochastic process theory. Then a staggered-grid pseudo-spectral method is used to simulate wave propagation. Scattering attenuation is estimated by the spectral ratio method based on virtual detector records. Random elastic media numerical scatter results with various heterogeneity levels show that the higher heterogeneous levels cause greater scattering attenuation. When the scatter sizes are smaller than a wave length, the larger scatters give a greater attenuation. Finally, we propose a method to evaluate fluid-flow attenuation in porous media. The fluid-flow attenuation is derived from total attenuation and scattering attenuation in random porous media and the attenuation is estimated quantitatively. Results show that in the real seismic frequency range when the heterogeneous scale is about 101 meters (less than one wave length), scattering attenuation is larger than fluid-flow attenuation in random porous media and scattering attenuation is the main factor of seismic attenuation in real heterogeneous porous media.

INITIAL ENLARGEMENT IN A MARKOV CHAIN MARKET MODEL

Enlargement of filtrations is a classical topic in the general theory of stochastic processes. This theory has been applied to stochastic finance in order to analyze models with insider information. In this paper we study initial enlargement in a Markov chain market model, introduced by Norberg. In the enlarged filtration, several things can happen: some of the jumps times can be accessible or predictable, but in the original filtration all the jumps times are totally inaccessible. But even if the jumps times change to accessible or predictable, the insider does not necessarily have arbitrage possibilities.

MARM Processes Part II: The Empirically-Based Subclass

MARM (Multivariate Autoregressive Modular) processes constitute a versatile class of multidimensional stochastic sequences which can exactly fit arbitrary multi-dimensional empirical histograms and approximately fit the leading empirical autocorrelations and cross-correlations. A companion paper (Part I) presented the general theory of MARM processes. This paper (Part II) proposes practical MARM modeling and forecasting methodologies of considerable generality, suitable for implementation on a computer. The purpose of Part II is twofold: (1) to specialize the general class of MARM processes to a practical subclass, called Empirically-Based MARM (EB-MARM) processes, suitable for modeling of empirical vector-valued time series, and devise the corresponding fitting and forecasting algorithms; and (2) to illustrate the efficacy of the EB-MARM fitting and forecasting algorithms. Specifically, we shall consider MARM processes with iid step-function innovation densities and distortions based on an empirical multi-dimensional histogram, as well as empirical autocorrelation and cross-correlation functions. Finally, we illustrate the efficacy of these methodologies with an example of a three-dimension time series vector, using a software environment, called MultiArmLab, which supports MARM modeling and forecasting.

An analysis of seismic attenuation in random porous media

The attenuation of seismic wave in rocks has been one of the interesting research topics, but till now no poroelasticity models can thoroughly explain the strong attenuation of wave in rocks. In this paper, a random porous medium model is designed to study the law of wave propagation in complex rocks based on the theory of Biot poroelasticity and the general theory of stochastic process. This model sets the density of grain, porosity, permeability and modulus of frame as random parameters in space, and only one fluid infiltrates in rocks for the sake of better simulation effect in line with real rocks in earth strata. Numerical simulations are implemented. Two different inverse quality factors of fast P-wave are obtained by different methods to assess attenuation through records of virtual detectors in wave field (One is amplitude decay method in time domain and the other is spectral ratio method in frequency domain). Comparing the attenuation results of random porous medium with those of homogeneous porous medium, we conclude that the attenuation of seismic wave of homogeneous porous medium is far weaker than that of random porous medium. In random porous media, the higher heterogeneous level is, the stronger the attenuation becomes, and when heterogeneity σ = 0.15 in simulation, the attenuation result is consistent with that by actual observation. Since the central frequency (50 Hz) of source in numerical simulation is in earthquake band, the numerical results prove that heterogeneous porous structure is one of the important factors causing strong attenuation in real stratum at intermediate and low frequency.

Private Global Regulation: The Politics of Setting Standards for International Product and Financial Markets

Divergent national standards often inhibit trade in goods and services. Consequently, the setting of international standards is rapidly becoming an issue of considerable economic and political salience. But who sets international standards? Who wins, who loses? Building on previous studies we have done, we are presenting in this paper a first general theory of international regulatory processes that take place in non-governmental institutional settings. Largely overlooked in IPE, these processes have moved to center-stage of global regulation as states have delegated ever-greater regulatory authority to transnational non-governmental bodies. Our theory provides a comprehensive account of how domestic or regional institutional features of private-sector groups systematically shape the politics of global regulation. We test a series of propositions on data collected by us through international business surveys over the last six years, covering both product and financial markets. We conclude by highlighting the challenges to global due process and democratic governance identified by our research.

Multiscale analysis of thermal field flow fractionation through macrotransport approach

We introduce the general theory of macrotransport processes to analyze thermal field flow fractionation (ThFFF). Multiscale analysis is carried out by adopting local and total moments. It is shown that the scheme of macrotransport processes is easier to apply and mathematically straightforward than exiting microscopic methods. The mean solute velocity and dispersivity associated with the retention time and standard deviation of a fractogram are calculated considering the temperature dependence of viscosity for ThFFF. A Gaussian distribution function is fitted to the fractogram measured experimentally and then the Soret numbers are calculated by using the retention time and standard deviation.

Forms of emergent interaction in General Process Theory

General Process Theory (GPT) is a new (non-Whiteheadian) process ontology. According to GPT the domains of scientific inquiry and everyday practice consist of configurations of ‘goings-on’ or ‘dynamics’ that can be technically defined as concrete, dynamic, non-particular individuals called general processes. The paper offers a brief introduction to GPT in order to provide ontological foundations for research programs such as interactivism that centrally rely on the notions of ‘process,’ ‘interaction,’ and ‘emergence.’ I begin with an analysis of our common sense concept of activities, which plays a crucial heuristic role in the development of the notion of a general process. General processes are not individuated in terms of their location but in terms of ‘what they do,’ i.e., in terms of their dynamic relationships in the basic sense of one process being part of another. The formal framework of GPT is thus an extensional mereology, albeit a non-classical theory with a non-transitive part-relation. After a brief sketch of basic notions and strategies of the GPT-framework I show how the latter may be applied to distinguish between causal, mechanistic, functional, self-maintaining, and recursively self-maintaining interactions, all of which involve ‘emergent phenomena’ in various senses of the term.

Toward a General Theory of Unconscious Processes in Psychoanalysis and Anesthesiology

Psychoanalysis and anesthesiology appear radically different in their clinical practice, yet they share a focus of inquiry: unconscious processes. Despite this common domain, there has been no exploration of the relationship between "the unconscious" as conceived by psychoanalysts and "surgical unconsciousness" as conceived by anesthesiologists. This is likely due to the fact that general anesthesia has been assumed to be a state in which the brain is simply "turned off." More recent neuroscientific data invalidate this assumption by demonstrating that the anesthetized brain is both cognitively dynamic and capable of implicit learning. Current perspectives on anesthetic mechanisms suggest that general anesthesia is characterized not simply by the absence of cognitive activity, but by the disintegration of cognitive activity. The cognitive unbinding paradigm of general anesthesia is discussed and its application to Wilfred Bion's theory of thinking, as well as his concept of attacks on linking, is elucidated. Based on the common structure and function of unconscious processes in psychoanalysis and anesthesiology, the foundation of a general theory is established.

Modelling to forestall extinction of Australian tropical birds

The tropics support some of the world’s richest centres of species diversity and endemism, yet these biomes are now dangerously imperilled by anthropogenic change. If we are to avert, or at least mitigate, catastrophic loss of species in these areas, it is vital to understand the direct and indirect effects of these agents of threat. Moreover, provision of a robust theoretical and empirical underpinning for the relationship between evolved characteristics (life-history traits and ecological preferences) and extinction risk may provide a general theory of extinction process that would be useful for conservation management. Birds provide some of the best quantitative data in tropical regions on the rate and selectivity of extinctions. Also, their autecology is better known than most other tropical taxonomic groups, making them ideal candidates for the application and testing of extinction theory and viability models. Using the relatively well-studied avifauna of the Australian tropics as an example, we apply population, habitat and threat data based on observed trends, in combination with various lines of surrogate information, to develop a predictive framework for extinction vulnerability of the region’s birds. Our inferences are based on generalized linear mixed modelling from an a priori set of models that include range, population, life history and threat variables. The best-selected model, with 92% of the AIC weight of evidence, was then used to rank the 387 species in terms of relative threat. We compare these results with predictions based on trends in habitat alone, and consider the likely impact of projected future threats during the twenty-first century.