Optimal Foraging Strategies Trever, Costas and Bill “International team of mystery” Plants Virtuatum computata. Simulate the movement of insects on a ring.

Slides:



Advertisements
Similar presentations
Mobility Increase the Capacity of Ad-hoc Wireless Network Matthias Gossglauser / David Tse Infocom 2001.
Advertisements

Hadi Goudarzi and Massoud Pedram
Gibbs Sampling Methods for Stick-Breaking priors Hemant Ishwaran and Lancelot F. James 2001 Presented by Yuting Qi ECE Dept., Duke Univ. 03/03/06.
Macroscopic ODE Models of Traffic Flow Zhengyi Zhou 04/01/2010.
CHE 185 – PROCESS CONTROL AND DYNAMICS
Using Virtual Laboratories to Teach Mathematical Modeling Glenn Ledder University of Nebraska-Lincoln
Chapter 3 Dynamic Modeling.
Sampling Distributions (§ )
The Matching Law Richard J. Herrnstein. Reinforcement schedule Fixed-Ratio (FR) : the first response made after a given number of responses is reinforced.
Generated Waypoint Efficiency: The efficiency considered here is defined as follows: As can be seen from the graph, for the obstruction radius values (200,
Exp.Curve Planed tasks Prediction of behavior for each task! We should determine the time constant τ Rising exponential Falling exponential.
Smoothing 3D Meshes using Markov Random Fields
Artificial Learning Approaches for Multi-target Tracking Jesse McCrosky Nikki Hu.
CAS 1999 Dynamic Financial Analysis Seminar Chicago, Illinois July 19, 1999 Calibrating Stochastic Models for DFA John M. Mulvey - Princeton University.
Bart van Greevenbroek.  Authors  The Paper  Particle Swarm Optimization  Algorithm used with PSO  Experiment  Assessment  conclusion.
A gentle introduction to Gaussian distribution. Review Random variable Coin flip experiment X = 0X = 1 X: Random variable.
Ant Colonies As Logistic Processes Optimizers
Expectation Maximization Algorithm
Code and Decoder Design of LDPC Codes for Gbps Systems Jeremy Thorpe Presented to: Microsoft Research
Ch. 6 The Normal Distribution
Probability theory 2010 Conditional distributions  Conditional probability:  Conditional probability mass function: Discrete case  Conditional probability.
The moment generating function of random variable X is given by Moment generating function.
Computer vision: models, learning and inference
Dynamic Optimization Dr
Analytic Prediction of Emergent Dynamics for ANTS James Powell, Todd Moon and Dan Watson Utah State University.
Introduction to Monte Carlo Methods D.J.C. Mackay.
Moment Generating Functions 1/33. Contents Review of Continuous Distribution Functions 2/33.
2014 YU-ANTL Lab Seminar Performance Analysis of the IEEE Distributed Coordination Function Giuseppe Bianchi April 12, 2014 Yashashree.
Copyright © 2008 Pearson Education, Inc., publishing as Pearson Benjamin Cummings Population Ecology.
Introduction to Discrete Event Simulation Customer population Service system Served customers Waiting line Priority rule Service facilities Figure C.1.
EQUILIBRIUM!! Aims: To know a variety of strategies to solve equilibrium problems.
Random variables Petter Mostad Repetition Sample space, set theory, events, probability Conditional probability, Bayes theorem, independence,
BsysE595 Lecture Basic modeling approaches for engineering systems – Summary and Review Shulin Chen January 10, 2013.
Moment Generating Functions
Traffic Modeling.
Chapter 14 Monte Carlo Simulation Introduction Find several parameters Parameter follow the specific probability distribution Generate parameter.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 4 Curve Fitting.
MA354 Mathematical Modeling T H 2:45 pm– 4:00 pm Dr. Audi Byrne.
Discrete Probability Distributions. Random Variable Random variable is a variable whose value is subject to variations due to chance. A random variable.
Ecology 8310 Population (and Community) Ecology Patch selection (e.g., Marginal Value Theorem) Prey selection (optimal diet theory) Moving beyond feeding.
Optimal mechanisms (part 2) seminar in auctions & mechanism design Presentor : orel levy.
- 1 - Bayesian inference of binomial problem Estimating a probability from binomial data –Objective is to estimate unknown proportion (or probability of.
Time-dependent Schrodinger Equation Numerical solution of the time-independent equation is straightforward constant energy solutions do not require us.
Trade-offs Between Mobility and Density for Coverage in Wireless Sensor Networks Wei Wang, Vikram Srinivasan and Kee-Chaing Chua National University of.
Example: Bioassay experiment Problem statement –Observations: At each level of dose, 5 animals are tested, and number of death are observed.
Monte Carlo Process Risk Analysis for Water Resources Planning and Management Institute for Water Resources 2008.
M.E. Biagini, M. Boscolo, T. Demma (INFN-LNF) A. Chao, M.T.F. Pivi (SLAC). Status of Multi-particle simulation of INFN.
Lec. 08 – Discrete (and Continuous) Probability Distributions.
Lecture 2: Statistical learning primer for biologists
The generalization of Bayes for continuous densities is that we have some density f(y|  ) where y and  are vectors of data and parameters with  being.
MA354 An Introduction to Math Models (more or less corresponding to 1.0 in your book)
1 Motion Fuzzy Controller Structure(1/7) In this part, we start design the fuzzy logic controller aimed at producing the velocities of the robot right.
Short Introduction to Particle Filtering by Arthur Pece [ follows my Introduction to Kalman filtering ]
Computational Biology, Part 14 Recursion Relations Robert F. Murphy Copyright  1996, 1999, 2000, All rights reserved.
The role of market impact and investor behavior on fund flows Yoni and Doyne 9/2/09.
Visual Tracking by Cluster Analysis Arthur Pece Department of Computer Science University of Copenhagen
MA354 Math Modeling Introduction. Outline A. Three Course Objectives 1. Model literacy: understanding a typical model description 2. Model Analysis 3.
Maximum likelihood estimators Example: Random data X i drawn from a Poisson distribution with unknown  We want to determine  For any assumed value of.
Central Limit Theorem Let X 1, X 2, …, X n be n independent, identically distributed random variables with mean  and standard deviation . For large n:
“Cobweb” diagrams. Affine Difference Equations---Slope bigger than 1.
Active Walker Model for Bacterial Colonies: Pattern Formation and Growth Competition Shane Stafford Yan Li.
Intra-Beam scattering studies for CLIC damping rings A. Vivoli* Thanks to : M. Martini, Y. Papaphilippou *
Computational Physics (Lecture 11) PHY4061. Variation quantum Monte Carlo the approximate solution of the Hamiltonian Time Independent many-body Schrodinger’s.
Statistical Modelling
Probability Theory and Parameter Estimation I
Advanced Statistical Computing Fall 2016
The Matching Hypothesis
David Konrad Michel Pleimling 10/21/2011
11. Conditional Density Functions and Conditional Expected Values
11. Conditional Density Functions and Conditional Expected Values
Presentation transcript:

Optimal Foraging Strategies Trever, Costas and Bill “International team of mystery” Plants Virtuatum computata. Simulate the movement of insects on a ring of plants with varying quality Investigate the movement rules that maximize energy intake ZZZZZZZZZZZZ

Simulation Code Construction Plant Quality Qi Energy Ei Probability of not moving Pi Pi=Ei/(Ei+Eh) Through parameter Eh, the movement behavior of the insects can be changed The probabilities of moving left or right are Pil and Pir

Simulation Code Construction Plant Quality Qi Energy Ei Probability of not moving Pi Pi=Ei/(Ei+Eh) Through parameter Eh, the movement behavior of the insects can be changed Eh=0.1 Eh=1 Eh= Pi=Ei/(Ei+Eh) Through parameter Eh, the movement behavior of the insects can be changed The probabilities of moving left or right are Pil and Pir

Simulation Code Construction Plant Quality Qi Energy Ei Probability of not moving Pi Eh~0 Insects don’t move except when plant quality is extremely low Eh>1 Insects move continuously regardless of plant quality Eh=0.1 Eh=1 Eh=0.0001

Simulation Case#1-FIXED QUALITY Plant Position Plant Quality Eh= Eh=0. 1 Eh=1 Insects are uniformly distributed among plants at t=0

Simulation Case#1-FIXED QUALITY Plant Position Plant Quality Eh= Eh=0. 1 Eh=1

Simulation Case#1-FIXED QUALITY Plant Position Plant Quality Eh Average Energy Intake Optimal strategy is to NOT move unless plant the quality is very bad

Models for FIXED QUALITY Plants If we consider space as discrete but time as continuous, then movement can be modeled as m coupled ODE’s, where m is number of plants Equation for a single plant: where Since we are interested in equilibrium solutions, we set the system of ODE’s to zero.

Simulation Case#1-FIXED QUALITY Plant Position Plant Quality Eh Average Energy Intake Optimal strategy is to NOT move unless plant the quality is very bad Model Prediction Simulation Prediction

Simulation Case#2-FIXED QUALITY Plant Position Plant Quality Eh Average Energy Intake Optimal strategy is to NOT move unless plant the quality is very bad Model Prediction Simulation Prediction

Simulation Case#3-FIXED QUALITY Plant Position Plant Quality Eh Average Energy Intake Optimal strategy is to NOT move unless plant the quality is very bad Model Predictions For 100 random quality distributions Quality Generated Randomly

SUMMARY SCENARIO 1) Plant quality is fixed; Energy intake is density independent 2) Plant quality is fixed; Energy intake is density dependent 3) Plant quality is dynamic; Energy intake is density independent CONCLUSION 1)Optimal strategy: DON’T MOVE unless plant the quality is very bad 2) ? 3) ?

Simulation Case#1-FIXED QUALITY Plant Position Plant Quality Energy Intake rate is density dependent Ni Density Dependence

Simulation Case#1-FIXED QUALITY Plant Position Plant Quality Eh Average Energy Intake Optimal strategy is to NOT move unless plant the quality is very bad r=0 r=0.01 r=0.02 Energy Intake rate is density dependent

SUMMARY SCENARIO 1) Plant quality is fixed; Energy intake is density independent 2) Plant quality is fixed; Energy intake is density dependent 3) Plant quality is dynamic; Energy intake is density independent CONCLUSION 1)Optimal strategy: DON’T MOVE unless plant the quality is very bad 2)Optimal strategy: DON’T MOVE unless plant the quality is very bad 3) ?

Simulation Case#1-DYNAMIC QUALITY Plant Position Plant Quality Insects are uniformly distributed among plants at t=0 Quality Update: At every iteration the simulation encounters standardized constant growth and consumption of the plant by the present insects. INITIAL QUALITY

Simulation Case#1-DYNAMIC QUALITY Plant Position Plant Quality Eh= Eh=0. 1 Eh=1 Insects are uniformly distributed among plants at t=0 INITIAL QUALITY

Simulation Case#1-DYNAMIC QUALITY Plant Position Plant Quality Eh= Eh=0. 1 Eh=1 Quality Plot INITIAL QUALITY Quality Plot

Simulation Case#1-DYNAMIC QUALITY Plant Position Plant Quality Eh Average Energy Intake Simulation Results Optimal strategy is INTERMEDIATE between no movement and continuous movement

SUMMARY SCENARIO 1) Plant quality is fixed; Energy intake is density independent 2) Plant quality is fixed; Energy intake is density dependent 3) Plant quality is dynamic; Energy intake is density independent CONCLUSION 1)Optimal strategy: DON’T MOVE unless plant the quality is very bad 2)Optimal strategy: DON’T MOVE unless plant the quality is very bad 3) Optimal strategy: INTERMEDIATE between not moving and continuous movement

Optimal Foraging Strategies Trever, Costas and Bill “International team of mystery” “Oh, Behave…”