Universe 9886 from The Wolfram Physics Project

51 steps

wm9886`wm9886`

signature

2₃→3₃

rule

{{{1, 2, 3}, {1, 4, 5}} -> {{3, 6, 6}, {3, 5, 2}, {5, 1, 2}}}

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Basic Evolution

Basic evolution:

[◼]	WolframModel

[{{{1,2,3},{1,4,5}}{{3,6,6},{3,5,2},{5,1,2}}},{{1,1,1},{1,1,1}},6,"StatesPlotsList"]

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

Event-by-event evolution:

[◼]	WolframModel

[{{{1,2,3},{1,4,5}}{{3,6,6},{3,5,2},{5,1,2}}},{{1,1,1},{1,1,1}},<|"MaxEvents"6|>,"EventsStatesPlotsList"]

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

Vertex and edge counts:

{vertexCountList,edgeCountList}=

[◼]	WolframModel

[{{{1,2,3},{1,4,5}}{{3,6,6},{3,5,2},{5,1,2}}},{{1,1,1},{1,1,1}},500,{"VertexCountList","EdgeCountList"}];

ListLogPlot{vertexCountList,edgeCountList},

SequenceIcon



	vertices
	edges

Symbolic expression for edge count:

FindSequenceFunction[edgeCountList,t]

DifferenceRootFunction{y.,n.},2+4n.+(4-n.)y.[n.]+(-6+n.)y.[1+n.]0,y.[1]2,y.[2]3,y.[3]4,y.[4]6,y.[5]9,y.[6]13,y.[7]17[t]

Result after

generations:

WolframModel[

]

["FinalStatePlot"]

Causal Graph

Causal graph:

WolframModel[

]

"CausalGraph",

Rule[

]



Layered rendering:

WolframModel[

]

["LayeredCausalGraph"]

Causal graph distance matrix:

MatrixPlotTransposeGraphDistanceMatrix

WolframModel[

]

["CausalGraph"],

ListIcon



Final State Properties

Hypergraph adjacency matrix:

MatrixPlotAdjacencyMatrix@CatenateMapUndirectedEdge@@@Subsets[#,{2}]&,

WolframModel[

]

["FinalState"],

ListIcon



Vertex degree distribution:

HistogramValuesCountsCatenateUnion/@

WolframModel[

]

["FinalState"],

ListIcon

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Neighborhood volumes (ignoring directedness of connections):

volumes=

[◼]	RaggedMeanAround

Values

[◼]	HypergraphNeighborhoodVolumes



WolframModel[

]

["FinalState"],All,Automatic;

ListLogLogPlotvolumes,

ListIcon

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Effective dimension versus radius:

ListLinePlot

[◼]	LogDifferences

[volumes],

ListIcon



Successive neighborhood balls around a random vertex:

[◼]	HypergraphNeighborhoods



WolframModel[

]

["FinalState"],4

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

Distance matrix:

distanceMatrix=GraphDistanceMatrixUndirectedGraph

[◼]	HypergraphToGraph



WolframModel[

]

["FinalState"];

MatrixPlotExp[-(distanceMatrix/.0None)],

ListIcon



Distribution of distances in the graph:

HistogramFlatten[distanceMatrix],

ListIcon

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Spreading of Effects

Causal graph adjacency matrix:

MatrixPlotAdjacencyMatrix

WolframModel[

]

["CausalGraph"],

ListIcon



Neighborhood volumes in causal graph:

ListLogLogPlotValues

[◼]	GraphNeighborhoodVolumes



WolframModel[

]

["CausalGraph"],{1},

ListIcon



Other Evolution Orders

Random evolutions:

[◼]	WolframModel

[{{{1,2,3},{1,4,5}}{{3,6,6},{3,5,2},{5,1,2}}},{{1,1,1},{1,1,1}},<|"MaxEvents"195|>,"FinalStatePlot","EventOrderingFunction""Random"]

Different deterministic evolution orders:

[◼]	WolframModel

[{{{1,2,3},{1,4,5}}{{3,6,6},{3,5,2},{5,1,2}}},{{1,1,1},{1,1,1}},<|"MaxEvents"195|>,"EventOrderingFunction"{#,"LeastRecentEdge","RuleOrdering","RuleIndex"}]["FinalStatePlot",PlotLabel#]&/@{"OldestEdge","LeastOldEdge","LeastRecentEdge","NewestEdge","RuleOrdering","ReverseRuleOrdering"}

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