One problem, too many solutions: How costly is honest signalling of need?

The “cost of begging” is a prominent prediction of costly signalling theory, suggesting that offspring begging has to be costly in order to be honest. Seminal signalling models predict that there is a unique equilibrium cost function for the offspring that results in honest signalling and this cost function must be proportional to parent’s fitness loss. This prediction is only valid if signal cost and offspring condition is assumed to be independent. Here we generalize these models by allowing signal cost to depend on offspring condition. We demonstrate in the generalized model that any signal cost proportional to the fitness gain of the offspring also results in honest signalling. Moreover, we show that any linear combination of the two cost functions (one proportional to parent’s fitness loss, as in previous models, the other to offspring’s fitness gain) also leads to honest signalling in equilibrium, yielding infinitely many solutions. Furthermore, we demonstrate that there exist linear combinations such that the equilibrium cost of signals is negative and the signal is honest. Our results show that costly signalling theory cannot predict a unique equilibrium cost in signalling games of parent-offspring conflicts if signal cost depends on offspring condition. It follows, contrary to previous claims, that the existence of parent-offspring conflict does not imply costly equilibrium signals. As an important consequence, it is meaningless to measure the “cost of begging” as long as the dependence of signal cost on offspring condition is unknown. Any measured equilibrium cost in case of condition-dependent signal cost has to be compared both to the parent’s fitness loss and to the offspring’s fitness gain in order to provide meaningful interpretation

Assembly Theory Explains and Quantifies the Emergence of Selection and Evolution

Since the time of Darwin, scientists have struggled to reconcile the evolution of biological forms in a universe determined by fixed laws. These laws underpin the origin of life, evolution, human culture and technology, as set by the boundary conditions of the universe, however these laws cannot predict the emergence of these things. By contrast evolutionary theory works in the opposite direction, indicating how selection can explain why some things exist and not others. To understand how open-ended forms can emerge in a forward-process from physics that does not include their design, a new approach to understand the non-biological to biological transition is necessary. Herein, we present a new theory, Assembly Theory (AT), which explains and quantifies the emergence of selection and evolution. In AT, the complexity of an individual observable object is measured by its Assembly Index (a), defined as the minimal number of steps needed to construct the object from basic building blocks. Combining a with the copy number defines a new quantity called Assembly which quantifies the amount of selection required to produce a given ensemble of objects. We investigate the internal structure and properties of assembly space and quantify the dynamics of undirected exploratory processes as compared to the directed processes that emerge from selection. The implementation of assembly theory allows the emergence of selection in physical systems to be quantified at any scale as the transition from undirected-discovery dynamics to a selected process within the assembly space. This yields a mechanism for the onset of selection and evolution and a formal approach to defining life. Because the assembly of an object is easily calculable and measurable it is possible to quantify a lower limit on the amount of selection and memory required to produce complexity uniquely linked to biology in the universe.Comment: 22 pages, 7 figure

Phase space volume scaling of generalized entropies and anomalous diffusion scaling governed by corresponding non-linear Fokker-Planck equations

Many physical, biological or social systems are governed by history-dependent dynamics or are composed of strongly interacting units, showing an extreme diversity of microscopic behaviour. Macroscopically, however, they can be efficiently modeled by generalizing concepts of the theory of Markovian, ergodic and weakly interacting stochastic processes. In this paper, we model stochastic processes by a family of generalized Fokker-Planck equations whose stationary solutions are equivalent to the maximum entropy distributions according to generalized entropies. We show that at asymptotically large times and volumes, the scaling exponent of the anomalous diffusion process described by the generalized Fokker-Planck equation and the phase space volume scaling exponent of the generalized entropy bijectively determine each other via a simple algebraic relation. This implies that these basic measures characterizing the transient and the stationary behaviour of the processes provide the same information regarding the asymptotic regime, and consequently, the classification of the processes given by these two exponents coincide

Honesty in signalling games is maintained by trade-offs rather than costs

Background Signal reliability poses a central problem for explaining the evolution of communication. According to Zahavi’s Handicap Principle, signals are honest only if they are costly at the evolutionary equilibrium; otherwise, deception becomes common and communication breaks down. Theoretical signalling games have proved to be use- ful for understanding the logic of signalling interactions. Theoretical evaluations of the Handicap Principle are difficult, however, because finding the equilibrium cost function in such signalling games is notoriously complicated. Here, we provide a general solution to this problem and show how cost functions can be calculated for any arbitrary, pairwise asymmetric signalling game at the evolutionary equilibrium. Results Our model clarifies the relationship between signalling costs at equilibrium and the conditions for reliable signalling. It shows that these two terms are independent in both additive and multiplicative models, and that the cost of signalling at honest equilibrium has no effect on the stability of communication. Moreover, it demonstrates that honest signals at the equilibrium can have any cost value, even negative, being beneficial for the signaller inde- pendently of the receiver’s response at equilibrium and without requiring further constraints. Our results are general and we show how they apply to seminal signalling models, including Grafen’s model of sexual selection and Godfray’s model of parent-offspring communication. Conclusions Our results refute the claim that signals must be costly at the evolutionary equilibrium to be reliable, as predicted by the Handicap Principle and so-called ‘costly signalling’ theory. Thus, our results raise serious concerns about the handicap paradigm. We argue that the evolution of reliable signalling is better understood within a Darwin- ian life-history framework, and that the conditions for honest signalling are more clearly stated and understood by evaluating their trade-offs rather than their costs per se. We discuss potential shortcomings of equilibrium models and we provide testable predictions to help advance the field and establish a better explanation for honest signals. Last but not least, our results highlight why signals are expected to be efficient rather than wasteful

Novelty and imitation within the brain: a Darwinian neurodynamic approach to combinatorial problems.

Efficient search in vast combinatorial spaces, such as those of possible action sequences, linguistic structures, or causal explanations, is an essential component of intelligence. Is there any computational domain that is flexible enough to provide solutions to such diverse problems and can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of neural informational patterns, is a promising candidate. Here we implement imperfect information copying through one reservoir computing unit teaching another. Teacher and learner roles are assigned dynamically based on evaluation of the readout signal. We demonstrate that the emerging Darwinian population of readout activity patterns is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We introduce a novel analysis method, neural phylogenies, that displays the unfolding of the neural-evolutionary process

On the shape of semantic space - what can we infer from large-scale statistical properties of texts?

[eng] The large amount of digitized linguistic data opens up the unique possibility of using the methodology of complex systems to understand high-level human cognitive processes. Two such issues are i) the way we categorize the continuous space of real-world features into discrete concepts, and ii) the way we use language to copy a line a thought from one brain to another. In this work I address both questions by formulating a simple text generation model which reproduces the three major characteristic large-scale statistical laws of human language streams, namely Zipf’s law, Heaps’ law and Burstiness. Furthermore, the generation itself can be described as a random walk on a scale-free, highly clustered and low dimensional complex network, suggesting that this class of networks is appropriate as a minimal model of the semantic space. Entangling the global characteristics of the semantic space is an inevitable step towards analyzing texts as trajectories in such a space, with promising applications such as author or style identification, personal disorder diagnosis, or the evolution of cultural traits mirrored by text production characteristics

On the shape of semantic space - what can we infer from large-scale statistical properties of texts?

Master’s degree in Physics of Complex Systems at the Universitat de les Illes Balears, academic year 2015/16.The large amount of digitized linguistic data opens up the unique possibility of using the methodology of complex systems to understand high-level human cognitive processes. Two such issues are i) the way we categorize the continuous space of real-world features into discrete concepts, and ii) the way we use language to copy a line a thought from one brain to another. In this work I address both questions by formulating a simple text generation model which reproduces the three major characteristic large-scale statistical laws of human language streams, namely Zipf’s law, Heaps’ law and Burstiness. Furthermore, the generation itself can be described as a random walk on a scale-free, highly clustered and low dimensional complex network, suggesting that this class of networks is appropriate as a minimal model of the semantic space. Entangling the global characteristics of the semantic space is an inevitable step towards analyzing texts as trajectories in such a space, with promising applications such as author or style identification, personal disorder diagnosis, or the evolution of cultural traits mirrored by text production characteristics.Peer reviewe

How many causal pathways must symptoms form before we call them a borderline? A hierarchical network model of borderline personality disorder

Borderline personality disorder (BPD) is characterized by impulsivity, emotion dysregulation, disturbed relationships, and identity disturbances. Despite the known variable co-occurrence of BPD symptoms, the possible causal relationships are not well understood. We addressed this by creating a hierarchical network model of BPD, which identifies the most likely acyclic causal pathways that are driving BPD development. Cross-sectional data was obtained from the Structured Clinical Interview-II (SCID-II), and possible causal relationships between symptoms were identified from conditional independence relations. The symptoms’ hierarchy values, assessing their role in causal pathways, was determined by a random walk-based algorithm. By analyzing the directed network of BPD symptoms, it was found that symptoms in initial stages of causal pathways were abandonment, physical fights, impulsivity, suicidal threats, identity disturbances, and affective instability. Based on the assessed role symptoms play in causal pathways of BPD development, specific symptoms can be targeted during early diagnosis and clinical assessment