Universe 94362 from The Wolfram Physics Project

39 steps

wm94362`wm94362`

signature

1₂2₃→4₂4₃

rule

{{{1, 2, 3}, {4, 5, 6}, {1, 4}} -> {{3, 7, 8}, {9, 2, 10}, {11, 5, 12}, {13, 6, 14}, {8, 14}, {10, 7}, {12, 9}, {13, 11}}}

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Basic Evolution

Basic evolution:

[◼]	WolframModel

[{{{1,2,3},{4,5,6},{1,4}}{{3,7,8},{9,2,10},{11,5,12},{13,6,14},{8,14},{10,7},{12,9},{13,11}}},{{1,1,1},{1,1,1},{1,1}},6,"StatesPlotsList"]

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

Event-by-event evolution:

[◼]	WolframModel

[{{{1,2,3},{4,5,6},{1,4}}{{3,7,8},{9,2,10},{11,5,12},{13,6,14},{8,14},{10,7},{12,9},{13,11}}},{{1,1,1},{1,1,1},{1,1}},<|"MaxEvents"6|>,"EventsStatesPlotsList"]

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

Vertex and edge counts:

{vertexCountList,edgeCountList}=

[◼]	WolframModel

[{{{1,2,3},{4,5,6},{1,4}}{{3,7,8},{9,2,10},{11,5,12},{13,6,14},{8,14},{10,7},{12,9},{13,11}}},{{1,1,1},{1,1,1},{1,1}},500,{"VertexCountList","EdgeCountList"}];

ListLogPlot{vertexCountList,edgeCountList},



	vertices
	edges

Symbolic expression for edge count:

FindSequenceFunction[edgeCountList,t]

-2+5t

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"],



Final State Properties

Hypergraph adjacency matrix:

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

WolframModel[

]

["FinalState"],



Vertex degree distribution:

HistogramValuesCountsCatenateUnion/@

WolframModel[

]

["FinalState"],



Neighborhood volumes (ignoring directedness of connections):

volumes=

[◼]	RaggedMeanAround

Values

[◼]	HypergraphNeighborhoodVolumes



WolframModel[

]

["FinalState"],All,Automatic;

ListLogLogPlotvolumes,



Effective dimension versus radius:

ListLinePlot

[◼]	LogDifferences

[volumes],



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)],



Distribution of distances in the graph:

HistogramFlatten[distanceMatrix],



Spreading of Effects

Causal graph adjacency matrix:

MatrixPlotAdjacencyMatrix

WolframModel[

]

["CausalGraph"],



Neighborhood volumes in causal graph:

ListLogLogPlotValues

[◼]	GraphNeighborhoodVolumes



WolframModel[

]

["CausalGraph"],{1},



Other Evolution Orders

Random evolutions:

[◼]	WolframModel

[{{{1,2,3},{4,5,6},{1,4}}{{3,7,8},{9,2,10},{11,5,12},{13,6,14},{8,14},{10,7},{12,9},{13,11}}},{{1,1,1},{1,1,1},{1,1}},<|"MaxEvents"39|>,"FinalStatePlot","EventOrderingFunction""Random"]

Different deterministic evolution orders:

[◼]	WolframModel

[{{{1,2,3},{4,5,6},{1,4}}{{3,7,8},{9,2,10},{11,5,12},{13,6,14},{8,14},{10,7},{12,9},{13,11}}},{{1,1,1},{1,1,1},{1,1}},<|"MaxEvents"39|>,"EventOrderingFunction"{#,"LeastRecentEdge","RuleOrdering","RuleIndex"}]["FinalStatePlot",PlotLabel#]&/@{"OldestEdge","LeastOldEdge","LeastRecentEdge","NewestEdge","RuleOrdering","ReverseRuleOrdering"}

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