HASH

New York, NYhash.ai
Based in London and New York, with a team around the world, our mission is to ensure that everybody has the information, tools, and knowledge they need to make the best decisions possible.... 

Public Listings

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Behavior
2023
Remove Self
@hash/remove_self.js
Send a message for this agent to remove itself after this step.
Behavior
1102
Create Agents
@hash/create_agents.js
Send create_agent messages
Behavior
640
Create Scatters
@hash/create_scatters.js
Create one or more agents at random positions within the bounds
Simulation
362
Wildfires - Regrowth
This model simulates the spread of wildfires in a regrowing forest. All trees grow over time, and have a small chance of being struck by lightning. If a tree is struck by lightning, or is adjacent to a fire, it sets alight. After burning for one step, the tree is reduced to an 'ember'. Embers have a small chance of regrowing into a new tree each step, and that chance increases linearly with the count of its adjacent trees. Analysis In this model, we can play with the effects of changing forest density, regrowth rate, and lighting probability in order to observe the health of our 'regrowing' forest. Consider what metrics we might evaluate to determine the health of our forest: Average tree height and the number of trees in our forest exhibit periodic fluctuations. We could assess the frequency of these fluctuations or their amplitude. If we define a "wildfire" as a step in the model during which there are more than a certain critical percentage of trees on fire, we can assess the frequency with which they occur. Is it periodic, or does the time between them increase? Extension See also the unbounded Forest model.
Behavior
260
Create Stacks
@hash/create_stacks.js
Create one or more agents at the same position
Behavior
228
Create Grids
@hash/create_grids.js
Create agents that are evenly distributed over the topology
Simulation
129
Warehouse Logistics
A basic warehouse model. Items can be stored on "shelf" agents, and "worker" agents pick up and place items on the shelves and on the "dock" agents.
Simulation
110
City Infection Model
This model simulates how a virus spreads through a human population. Green agents (healthy people) have a chance of becoming sick if they are within the search radius of a red agent (sick person). Each step, sick agents have a chance to recover or a chance of dying. If an agent dies it's removed from the map. If an agent recovers they are rendered grey and are immune to the disease until their immunity wears out at which point they become green, healthy agents again. If an agent is infected, after timetosymptoms the agent will request a test from the Hospital. If the test catches a true positive, the agent learns they are sick and, depending on properties of the simulation run, will with some likelihood stay home. If it's a severe case and the hospital has capacity, they will stay at the hospital. Agents move around the map, clustering near homes, grocery stores, and offices. The map loosely reflects the population density of San Francisco, in that Census GIS data informed the distribution of homes around the map.
Behavior
78
Random Movement
@hash/random_movement.rs
This behavior causes an agent to move by a random amount. This amount is defined in randommovementstep_size (and defaults to 1). If randommovementseekminneighbors or randommovementseekmaxneighbors is defined, then the agent will stop moving if it has an amount of neighbors falling in the specified range. Example This agent will move 0 or 3 spaces up/down and left/right, until it has at least 5 neighbors: const attentionSeekingAgent = { behaviors: ["random_movement"], position: [6, 3], randommovementstep_size: 3, randommovementseekminneighbors: 5 } `
Dataset
71
Sugarscape Map
@hash/sugarscape-map
A 50x50 CSV map defining the strength of sugar patches for the classic "Sugarscape" model by Epstein and Axtell.