The philosophical interpretation of language game theory

2021 ◽  
Author(s):  
Nick Zangwill
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Language Game ◽  
Linguistic Meaning ◽  
Theoretic Approach ◽  
Philosophical Interpretation ◽  
Game Theoretic ◽  
Game Theoretic Approach

Abstract I give an informal presentation of the evolutionary game theoretic approach to the conventions that constitute linguistic meaning. The aim is to give a philosophical interpretation of the project, which accounts for the role of game theoretic mathematics in explaining linguistic phenomena. I articulate the main virtue of this sort of account, which is its psychological economy, and I point to the casual mechanisms that are the ground of the application of evolutionary game theory to linguistic phenomena. Lastly, I consider the objection that the account cannot explain predication, logic, and compositionality.

scholarly journals Quantifying the Role of Homophily in Human Cooperation Using Multiplex Evolutionary Game Theory

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140646 ◽  
Author(s):  
Alessandro Di Stefano ◽  
Marialisa Scatà ◽  
Aurelio La Corte ◽  
Pietro Liò ◽  
Emanuele Catania ◽  
...  
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game

Game Models in Various Applications

2014 ◽  
pp. 44-128 ◽  
Keyword(s):  
Game Theory ◽  
Academic Research ◽  
Viable Alternative ◽  
Theoretic Approach ◽  
Original Game ◽  
Technical Problems ◽  
Research Areas ◽  
History Of ◽  
Game Theoretic ◽  
Game Theoretic Approach

Recently, game-theoretic models have become famous in many academic research areas. Therefore, many applications and extensions of the original game theoretic approach appear in many of the major science fields. Despite all the technical problems, the history of game theory suggests that it would be premature to abandon the tool, especially in the absence of a viable alternative. If anything, the development of game theory has been driven precisely by the realization of its limitations and attempts to overcome them. This chapter explores these ideas.

Learning About the Effects of Alert Uncertainty in Attack and Defend Decisions via Cognitive Modeling

2020 ◽  
pp. 001872082094542
Author(s):  
Palvi Aggarwal ◽  
Frederic Moisan ◽  
Cleotilde Gonzalez ◽  
Varun Dutt
Keyword(s):  
Cognitive Modeling ◽  
Training Dataset ◽  
Theoretic Approach ◽  
Cognitive Mechanisms ◽  
Test Dataset ◽  
Detection Systems ◽  
Instance Based Learning ◽  
Game Theoretic ◽  
Game Theoretic Approach

Objective We aim to learn about the cognitive mechanisms governing the decisions of attackers and defenders in cybersecurity involving intrusion detection systems (IDSs). Background Prior research has experimentally studied the role of the presence and accuracy of IDS alerts on attacker’s and defender’s decisions using a game-theoretic approach. However, little is known about the cognitive mechanisms that govern these decisions. Method To investigate the cognitive mechanisms governing the attacker’s and defender’s decisions in the presence of IDSs of different accuracies, instance-based learning (IBL) models were developed. One model (NIDS) disregarded the IDS alerts and one model (IDS) considered them in the instance structure. Both the IDS and NIDS models were trained in an existing dataset where IDSs were either absent or present and they possessed different accuracies. The calibrated IDS model was tested in a newly collected test dataset where IDSs were present 50% of the time and they possessed different accuracies. Results Both the IDS and NIDS models were able to account for human decisions in the training dataset, where IDS was absent or present and it possessed different accuracies. However, the IDS model could accurately predict the decision-making in only one of the several IDS accuracy conditions in the test dataset. Conclusions Cognitive models like IBL may provide some insights regarding the cognitive mechanisms governing the decisions of attackers and defenders in conditions not involving IDSs or IDSs of different accuracies. Application IBL models may be helpful for penetration testing exercises in scenarios involving IDSs of different accuracies.

scholarly journals eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning

2020 ◽  
Vol 4 (4) ◽  
pp. 37
Author(s):  
Khaled Fawagreh ◽  
Mohamed Medhat Gaber
Keyword(s):  
Random Forest ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Theoretic Approach ◽  
Healthcare Applications ◽  
Healthcare Data ◽  
Smart Healthcare ◽  
Game Theoretic ◽  
Game Theoretic Approach

To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.

scholarly journals A Game Theoretic Approach to Swarm Robotics

2006 ◽  
Vol 3 (3) ◽  
pp. 131-142 ◽  
Author(s):  
S. N. Givigi ◽  
H. M. Schwartz
Keyword(s):  
Game Theory ◽  
Reinforcement Learning ◽  
Personality Traits ◽  
Evolutionary Psychology ◽  
Learning Process ◽  
Swarm Robotics ◽  
Theoretic Approach ◽  
Intelligent Robots ◽  
Game Theoretic ◽  
Game Theoretic Approach

In this article, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced, and some simulations showing its potential are reported.

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