[{"id":349,"title":"Playing Games with Ideas: when epistemology pays off","sha":"e4702f43c1c9f3561e57b3ab001a924b","user_id":null,"state":"published","body":"In standard game theory analysis, gameplay proceeds using rational strategies. While there is no explicit link between rationality and strategy selection, playing to equilibrium solutions often depends on choosing the optimal strategy. Yet what if strategy optimality is not obvious to the players? Often, people make seemingly irrational decisions based on beliefs or situational interpretations of the empirical world. These beliefs have high social import, but with little advantage compared to empirical reality. \r\n\r\nWe use a decision kernel to model how beliefs (an epistemological network) influence a rational strategy suite (top box in attachment). The feedforward network output is a set of beliefs that influence gameplay as a set of rational behavior (empirical strategy suite). This results in an epistemological-influenced social capital payoff function, and an empirical payoff function (bottom box in attachment). The joint distribution (or area of intersection between the payoff functions) is the epistemological payoff. This epistemological payoff can be small (low conditional social payoff) or large (high conditional social payoff). When small, players play their strategies close to the optimal expectation. Larger epistemological payoffs result plays that have with relatively higher social utility, but also little immediate utility when compared to the empirical world.","created_at":"2016-11-21T06:06:01.489Z","updated_at":"2016-11-21T11:38:15.392Z","zenodo_id":167647,"doi":"https://doi.org/10.5281/zenodo.167647","subject":null,"tags":["cognition","game theory","cultural evolution","complex systems","theory","philosophy"],"vote_count":1,"view_count":1,"score":1.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":"whole-figure.png","attachment_content_type":"image/png","attachment_file_size":286497,"attachment_updated_at":"2016-11-21T06:06:01.443Z"},{"id":880,"title":"Stage Structured Hybrid Model for Complex Systems Modelling","sha":"4051909f2fe237e3dad1a1acce5343ad","user_id":null,"state":"published","body":"Stochasticity and spatial distribution of pathogens play a critical role in determining the outcome of an infection. 1 in a million immune system cells are specific to a particular pathogen. The serendipitous encounter of rare immune system cells with its fated antigen can determine the mortality of the infected animal. Moreover pathogens may remain initially localized in a small volume of tissue. Hence stochastic and spatial aspects play an important role in pathogenesis, especially early on in the infection. Current efforts at investigating the effect of stochasticity and space in modeling of host immune response and pathogens use agent based models (ABMs). However these are computationally expensive. Population level approaches like ordinary differential equations (ODEs) are computationally tractable. However they make simplifying assumptions that are unlikely to be true early on in the infection. I propose a stage-structured hybrid model that strikes a balance between the detail of representation of an ABM and the computational tractability of an ODE model. It uses a spatially explicit ABM in the initial stage of infection, and a coarse-grained, computationally tractable ODE model in the latter stages of infection. Such an approach might hold promise in modeling of infectious diseases and other complex systems.","created_at":"2021-09-13T14:44:32.582Z","updated_at":"2021-11-01T11:48:02.906Z","zenodo_id":5504398,"doi":"https://doi.org/10.5281/zenodo.5504398","subject":null,"tags":["hybrid model","complex systems"],"vote_count":2,"view_count":1,"score":2.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null},{"id":103,"title":"Develop a human-readable formal language for complex scientific models","sha":"27deb8bb6d86c83d29f53320f02a22a5","user_id":250,"state":"published","body":"Much modern research is based on complex models for complex systems. Examples are biological macromolecules, climate models, or systems biology. These models are too complex for publishing in journal articles. They currently exist only in the heads of a few experts and embedded in simulation software, inaccessible for inspection by peers. Most users of simulation software have no precise knowledge of the models they are applying. See [here](http://dx.doi.org/10.12688/f1000research.3978.2) for an in-depth discussion.\r\n\r\nSuch models should be encoded in [formal languages](http://en.wikipedia.org/wiki/Formal_language) designed for this purpose. In contrast to programming languages, these languages are designed for simplicity and readability by humans. Formalized complex models can then be used for testing simulation software, and read as input by future simulation software. They can also be worked on by software tools similar to today's computer algebra systems, and published as datasets.\r\n\r\nThe idea is similar to the use of [specification languages](http://en.wikipedia.org/wiki/Specification_language) for software. A specification for scientific software would consist of several models plus a task to be performed based on these models. A formal language for scientific models would be a hybrid of a software specification language and a notation like [OpenMath](http://www.openmath.org/).\r\n","created_at":"2015-02-27T08:56:20.512Z","updated_at":"2023-04-22T15:39:45.301Z","zenodo_id":23586,"doi":"http://dx.doi.org/10.5281/zenodo.15700","subject":null,"tags":["computational science","complex systems","formal methods"],"vote_count":26,"view_count":1,"score":26.1,"deleted":false,"muted":false,"tweeted":true,"attachment_file_name":null,"attachment_content_type":null,"attachment_file_size":null,"attachment_updated_at":null}]